Input: ['Great place to relax and enjoy your dinner']

Output: [['place', 'ambience general', 'positive', 'Great']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['$ 170 down the toilet ...']

Output: [['NULL', 'restaurant prices', 'negative', '$ 170']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['And amazingly cheap .']

Output: [['NULL', 'food prices', 'positive', 'cheap']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Good creative rolls !']

Output: [['rolls', 'food quality', 'positive', 'Good creative'], ['rolls', 'food style_options', 'positive', 'Good creative']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ["Delivery guy sometimes get upset if you do n't tip more than 10 % ."]

Output: [['Delivery guy', 'service general', 'negative', 'upset']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['I have been going back again and again .']

Output: [['NULL', 'restaurant general', 'positive', 'going back again and again']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['A different server enhanced the fun , dumping our entrees in front of us halfway through our appetizer ( which was delicious ) .']

Output: [['server', 'service general', 'negative', 'enhanced'], ['appetizer', 'food quality', 'positive', 'delicious']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['We liked it so much , that we will always make it a point to dine here when we visit New York .']

Output: [['NULL', 'restaurant general', 'positive', 'liked']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Most of the servers are very attentive , friendly and quite attractive .']

Output: [['servers', 'service general', 'positive', 'attentive'], ['servers', 'service general', 'positive', 'friendly'], ['servers', 'service general', 'positive', 'attractive']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ["it 's the only place you can get yummy authentic japanese comfort food ."]

Output: [['japanese comfort food', 'food quality', 'positive', 'yummy authentic']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['We never ate because by close to 2 in the monring we were not served and were too upset ad tired to start eating .']

Output: [['NULL', 'service general', 'negative', 'upset']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['As many other reviewers noticed , your order is often slow to arrive - this is particularly true in the evening but is not a problem during lunch time .']

Output: [['NULL', 'service general', 'neutral', 'slow']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Yes , the prices are high , but I felt it was worth it .']

Output: [['NULL', 'restaurant prices', 'negative', 'high'], ['NULL', 'restaurant general', 'positive', 'worth']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Love YUKA .']

Output: [['YUKA', 'restaurant general', 'positive', 'Love']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Over all the looks of the place exceeds the actual meals .']

Output: [['looks', 'ambience general', 'positive', 'exceeds']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ["You ca n't go wrong with this place ."]

Output: [['place', 'restaurant general', 'positive', "ca n't go wrong"]]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ["i 've been to sapphire twice and both times the food was fine , if not good ."]

Output: [['food', 'food quality', 'positive', 'fine']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['They are extremely rude , not even apologizing for the horrible service we got and handing us a bill well over $ 500 for some drinks adn their pita bread !']

Output: [['service', 'service general', 'negative', 'horrible'], ['drinks', 'drinks prices', 'negative', 'over $ 500'], ['pita bread', 'food prices', 'negative', 'over $ 500'], ['NULL', 'service general', 'negative', 'rude']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['I loved everythig about it-especially the shows and actors .']

Output: [['shows', 'ambience general', 'positive', 'loved'], ['actors', 'ambience general', 'positive', 'loved']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['I highly recommend to anyone to give this place a try .']

Output: [['place', 'restaurant general', 'positive', 'recommend']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['This is a fun restaurant to go to .']

Output: [['restaurant', 'restaurant general', 'positive', 'fun']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['A guaranteeed delight !']

Output: [['NULL', 'restaurant general', 'positive', 'delight']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['We were very pleasantly surprised .']

Output: [['NULL', 'restaurant general', 'positive', 'pleasantly surprised']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Conveniently located too , being right on Bedford ave .']

Output: [['NULL', 'location general', 'positive', 'Conveniently']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['However , it is jus too good to not praise it .']

Output: [['NULL', 'restaurant general', 'positive', 'good'], ['NULL', 'restaurant general', 'positive', 'praise']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['The service was excellent - friendly and attentive .']

Output: [['service', 'service general', 'positive', 'excellent'], ['service', 'service general', 'positive', 'friendly'], ['service', 'service general', 'positive', 'attentive']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['We have been to this place many times , and always have great food , wine , and service .']

Output: [['food', 'food quality', 'positive', 'great'], ['wine', 'drinks quality', 'positive', 'great'], ['service', 'service general', 'positive', 'great']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Not one of our meals was edible - bland and/or made with weird rosemary or orange flavoring .']

Output: [['meals', 'food quality', 'negative', 'edible'], ['rosemary or orange flavoring', 'food quality', 'negative', 'weird']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['This place is not inviting and the food is totally weird .']

Output: [['place', 'ambience general', 'negative', 'not inviting'], ['food', 'food quality', 'negative', 'weird']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['HIGHLY RECOMMENDED ! ! ! ! !']

Output: [['NULL', 'restaurant general', 'positive', 'RECOMMENDED']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Their tuna tartar appetizer is to die for .']

Output: [['tuna tartar appetizer', 'food quality', 'positive', 'die for']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['keep up the good work .']

Output: [['NULL', 'restaurant general', 'positive', 'good']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Not enough wines by the glass either .']

Output: [['wines by the glass', 'drinks style_options', 'negative', 'Not enough']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['The seats are uncomfortable if you are sitting against the wall on wooden benches .']

Output: [['seats', 'ambience general', 'negative', 'uncomfortable']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['The ambience is pretty and nice for conversation , so a casual lunch here would probably be best .']

Output: [['ambience', 'ambience general', 'positive', 'pretty'], ['ambience', 'ambience general', 'positive', 'nice'], ['NULL', 'restaurant miscellaneous', 'positive', 'best']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['So rushing us out was absolutely unnecessary !']

Output: [['NULL', 'service general', 'negative', 'rushing us out']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['For desserts , we tried the frozen black sesame mousse ( interesting but not extraordinary ) and matcha ( powdered green tea ) and blueberry cheesecake , which was phenomenal .']

Output: [['frozen black sesame mousse', 'food quality', 'neutral', 'interesting'], ['frozen black sesame mousse', 'food quality', 'neutral', 'not extraordinary'], ['matcha ( powdered green tea ) and blueberry cheesecake', 'food quality', 'positive', 'phenomenal']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Worth the trip from Manhattan .']

Output: [['NULL', 'restaurant general', 'positive', 'Worth']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['The price very reasonable .']

Output: [['NULL', 'restaurant prices', 'positive', 'reasonable']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ["Drawbacks : service is slow and they do n't toast !"]

Output: [['service', 'service general', 'negative', 'slow']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['They were very abrupt with me when I called and actually claimed the food was late because they were out of rice .']

Output: [['NULL', 'service general', 'negative', 'abrupt']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['I asked for an open faced cheese sandwich and the manager basically told me to take my business elsewhere !']

Output: [['manager', 'service general', 'negative', 'take my business elsewhere']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ["THE SERVICE IS AMAZING , i 've had different waiters and they were all nice , which is a rare thing in NYC ."]

Output: [['SERVICE', 'service general', 'positive', 'AMAZING'], ['waiters', 'service general', 'positive', 'nice']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['So I decide to report back to the waitress because it was completely inedible .']

Output: [['NULL', 'food quality', 'negative', 'inedible']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['I would highly recommend .']

Output: [['NULL', 'restaurant general', 'positive', 'highly recommend']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['The food was well prepared and the service impecable .']

Output: [['food', 'food quality', 'positive', 'well prepared'], ['service', 'service general', 'positive', 'impecable']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['The food is outstanding and the service is quick , friendly and very professional .']

Output: [['food', 'food quality', 'positive', 'outstanding'], ['service', 'service general', 'positive', 'quick'], ['service', 'service general', 'positive', 'friendly'], ['service', 'service general', 'positive', 'professional']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['They even scoop it out nice ( for those on a diet ) not too much not to little .']

Output: [['NULL', 'food style_options', 'positive', 'nice']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Best Indian Restaurant in the City']

Output: [['Indian Restaurant', 'restaurant general', 'positive', 'Best']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['From the spectacular caviar to the hospitable waitstaff , I felt like royalty and enjoyed every second of it .']

Output: [['caviar', 'food quality', 'positive', 'spectacular'], ['caviar', 'food quality', 'positive', 'enjoyed'], ['waitstaff', 'service general', 'positive', 'hospitable'], ['waitstaff', 'service general', 'positive', 'enjoyed']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['I agree that dining at Casa La Femme is like no other dining experience !']

Output: [['Casa La Femme', 'restaurant general', 'negative', 'like no other dining experience']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Best Indian Chinese in the city , by far !']

Output: [['Indian Chinese', 'food quality', 'positive', 'Best']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['There was a small wait , but shorter than I expected .']

Output: [['wait', 'service general', 'positive', 'small'], ['wait', 'service general', 'positive', 'shorter']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ["But after last night , Spice Grill is the only place I 'm eating indian cuisine ."]

Output: [['indian cuisine', 'food quality', 'positive', 'the only place']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ["The food is decent at best , and the ambience , well , it 's a matter of opinion , some may consider it to be a sweet thing , I thought it was just annoying ."]

Output: [['food', 'food quality', 'negative', 'decent'], ['ambience', 'ambience general', 'negative', 'annoying']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Personally I like the margherita pizza better , but they are all good .']

Output: [['margherita pizza', 'food quality', 'positive', 'like'], ['NULL', 'food quality', 'positive', 'good']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['If it seemed possible to do so while there I would have fought my bill since my dinner portion of my meal was inedible !']

Output: [['meal', 'food quality', 'negative', 'inedible'], ['NULL', 'restaurant prices', 'negative', 'fought my bill']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ["At first glance this place seems a bit pricey for a hot dog joint , but at Bark you do n't just get your average hot dog ."]

Output: [['Bark', 'restaurant prices', 'negative', 'pricey']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['The sashimi is always fresh and the rolls are innovative and delicious .']

Output: [['sashimi', 'food quality', 'positive', 'fresh'], ['rolls', 'food style_options', 'positive', 'innovative'], ['rolls', 'food quality', 'positive', 'delicious']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ["I ca n't wait to go back ."]

Output: [['NULL', 'restaurant general', 'positive', 'go back']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Delivery can be spot on or lacking depending on the weather and the day of the week .']

Output: [['Delivery', 'service general', 'negative', 'lacking']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['There are no negatives to speak of .']

Output: [['NULL', 'restaurant general', 'positive', 'no negatives']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['The Four Seasons restaurant is a great experience .']

Output: [['The Four Seasons restaurant', 'restaurant general', 'positive', 'great']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Seriously , this place kicks ass .']

Output: [['place', 'restaurant general', 'positive', 'kicks ass']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ["I 've enjoyed 99 % of the dishes we 've ordered with the only exceptions being the occasional too-authentic-for-me dish ( I 'm a daring eater but not THAT daring ) ."]

Output: [['dishes', 'food quality', 'positive', 'enjoyed'], ['dish', 'food quality', 'negative', 'too-authentic-for-me']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ["Ca n't wait to go back ."]

Output: [['NULL', 'restaurant general', 'positive', 'go back']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['amazing fresh dogs but best of all endless toppings ! ! !']

Output: [['dogs', 'food quality', 'positive', 'amazing fresh'], ['toppings', 'food style_options', 'positive', 'best'], ['toppings', 'food style_options', 'positive', 'endless']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Love this place , every time we are in the city this is one of the places we always go .']

Output: [['place', 'restaurant general', 'positive', 'Love']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Service is not what one would expect from a joint in this price category .']

Output: [['Service', 'service general', 'negative', 'not what one would expect']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['I stumbled upon this great pizzeria as I explored my new neighborhood .']

Output: [['pizzeria', 'restaurant general', 'positive', 'great']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['This quaint and romantic trattoria is at the top of my Manhattan restaurant list .']

Output: [['trattoria', 'ambience general', 'positive', 'quaint'], ['trattoria', 'restaurant general', 'positive', 'romantic']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['The food was delicious but do not come here on a empty stomach .']

Output: [['food', 'food quality', 'positive', 'delicious']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ["Patsy 's Pizza - true love"]

Output: [["Patsy 's Pizza", 'restaurant general', 'positive', 'true love']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Average to good Thai food , but terrible delivery .']

Output: [['Thai food', 'food quality', 'positive', 'Average to good'], ['delivery', 'service general', 'negative', 'terrible']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['I heartily recommend .']

Output: [['NULL', 'restaurant general', 'positive', 'recommend']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ["You can get a table without a reservation if you get there early I they do n't make you by bottles ."]

Output: [['NULL', 'service general', 'positive', 'get a table without a reservation']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['You are not eating haut cuisine with subtle hints of whatever but : Cassuolet , Steake Fritte , Tripe Stew , etc ; simple stuff .']

Output: [['NULL', 'food style_options', 'positive', 'simple'], ['NULL', 'food quality', 'positive', 'subtle hints of whatever']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['For the price , you can not eat this well in Manhattan .']

Output: [['NULL', 'restaurant prices', 'negative', 'can not eat this well']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['One of the best , if not THE best , restaurants in Park Slope ( and NY in general )']

Output: [['NULL', 'restaurant general', 'positive', 'best']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Always busy but fast moving .']

Output: [['NULL', 'service general', 'positive', 'fast moving']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['I tend to judge a sushi restaurant by its sea urchin , which was heavenly at sushi rose .']

Output: [['sea urchin', 'food quality', 'positive', 'heavenly']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Yakitori ( bbq meats ) is tasty too .']

Output: [['Yakitori ( bbq meats )', 'food quality', 'positive', 'tasty']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Whenever you need a Sushi fix , Mizu will be there with quality fish and great service .']

Output: [['fish', 'food quality', 'positive', 'quality'], ['service', 'service general', 'positive', 'great']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Faan is sooo good .']

Output: [['Faan', 'restaurant general', 'positive', 'good']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Fresh , mind blowing flavors .']

Output: [['NULL', 'food quality', 'positive', 'Fresh'], ['NULL', 'food quality', 'positive', 'mind blowing']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ["Do n't judge this place prima facie , you have to try it to believe it , a home away from home for the literate heart ."]

Output: [['place', 'restaurant general', 'positive', 'try it and believe it']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Fish is so very fresh .']

Output: [['Fish', 'food quality', 'positive', 'fresh']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['My boyfriend had Prime Rib it was good .']

Output: [['Prime Rib', 'food quality', 'positive', 'good']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Drinks way over priced .']

Output: [['Drinks', 'drinks prices', 'negative', 'over priced']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Unique apppetizers .']

Output: [['apppetizers', 'food quality', 'positive', 'Unique']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ["It 's a great place to order from or sit-in ."]

Output: [['place', 'restaurant miscellaneous', 'positive', 'great']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['The food is great and reasonably priced .']

Output: [['food', 'food quality', 'positive', 'great'], ['food', 'food prices', 'positive', 'reasonably priced']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['When we sat , we got great and fast service .']

Output: [['service', 'service general', 'positive', 'great'], ['service', 'service general', 'positive', 'fast']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Be sure to try the seasonal , and always delicious , specials .']

Output: [['specials', 'food quality', 'positive', 'try'], ['specials', 'food quality', 'positive', 'seasonal'], ['specials', 'food quality', 'positive', 'delicious']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['When we threatened to leave , we were offered a meager discount even though half the order was missing .']

Output: [['NULL', 'service general', 'negative', 'missing']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Great food , good size menu , great service and an unpretensious setting .']

Output: [['food', 'food quality', 'positive', 'Great'], ['menu', 'food style_options', 'positive', 'good size'], ['service', 'service general', 'positive', 'great'], ['setting', 'ambience general', 'positive', 'unpretensious']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['The sangria was pretty tasty and good on a hot muggy day .']

Output: [['sangria', 'drinks quality', 'positive', 'tasty'], ['sangria', 'drinks quality', 'positive', 'good']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Steamed fresh so brought hot hot hot to your table .']

Output: [['NULL', 'food quality', 'positive', 'fresh']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ["It just was n't Thai ."]

Output: [['NULL', 'food quality', 'negative', "was n't Thai"]]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['All the staff is absolutely professional ! !']

Output: [['staff', 'service general', 'positive', 'professional']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['The service was impeccable and unobtrusive -- the staff knows what they are there to do -- to know their menu , present your meal , and attend to your needs .']

Output: [['service', 'service general', 'positive', 'impeccable'], ['service', 'service general', 'positive', 'unobtrusive']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['A little pricey but it really hits the spot on a Sunday morning !']

Output: [['NULL', 'restaurant prices', 'negative', 'pricey '], ['NULL', 'restaurant general', 'positive', 'hits the spot']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ["The Four Seasons has history and it is a sort of landmark of New York City restaurants , but trust me , they will charge you through the nose just so that you can say `` I 've been to the four seasons restaurant `` ."]

Output: [['The Four Seasons', 'restaurant miscellaneous', 'neutral', 'a sort of landmark'], ['The Four Seasons', 'restaurant prices', 'negative', 'charge you through the nose']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['This was a great surprise .']

Output: [['NULL', 'restaurant general', 'positive', 'a great surprise']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ["The food can get pricey but the prixe fixe tasting menu is the greatest food for a good price and they cater the food to any food allergies or food you do n't like ."]

Output: [['food', 'food prices', 'negative', 'pricey'], ['prixe fixe tasting menu', 'food quality', 'positive', 'greatest'], ['prixe fixe tasting menu', 'food prices', 'positive', 'greatest']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ["I 'm not picky - but it was actually gross ."]

Output: [['NULL', 'food quality', 'negative', 'gross']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Food was very good , but not what I would consider out of this world .']

Output: [['Food', 'food quality', 'neutral', 'good']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['The server was really cool and served us our food and drinks with a smile .']

Output: [['server', 'service general', 'positive', 'cool']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['The manager came to the table and said we can do what we want , so we paid for what we did enjoy , the drinks and appetizers , and walked out .']

Output: [['drinks', 'drinks quality', 'positive', 'enjoy'], ['appetizers', 'food quality', 'positive', 'enjoy']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['This place is the most Japanese it can ever get .']

Output: [['place', 'restaurant miscellaneous', 'positive', 'Japanese']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['The portions are large and the servers always surprise us with a different starter .']

Output: [['portions', 'food style_options', 'positive', 'large']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['I absolutely Loved this place .']

Output: [['place', 'restaurant general', 'positive', 'Loved']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['The space is limited so be prepared to wait up to 45 minutes - 1 hour , but be richly rewarded when you savor the delicious indo-chinese food .']

Output: [['space', 'restaurant miscellaneous', 'negative', 'limited'], ['indo-chinese food', 'food quality', 'positive', 'delicious']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Nice value .']

Output: [['NULL', 'restaurant prices', 'positive', 'Nice value']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['I love it .']

Output: [['NULL', 'restaurant general', 'positive', 'love']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['They have a delicious banana chocolate dessert , as well as a great green tea tempura .']

Output: [['banana chocolate dessert', 'food quality', 'positive', 'delicious'], ['green tea tempura', 'food quality', 'positive', 'great']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['i love margherita pizza – i looove east village pizza']

Output: [['east village pizza', 'restaurant general', 'positive', 'love'], ['margherita pizza', 'food quality', 'positive', 'looove']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Here the hot dog is elevated to the level of a real entree with numerous variations available .']

Output: [['hot dog', 'food quality', 'positive', 'elevated'], ['hot dog', 'food style_options', 'positive', 'elevated']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['my favorite place lol']

Output: [['place', 'restaurant general', 'positive', 'favorite']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['The counter service is bad .']

Output: [['counter service', 'service general', 'negative', 'bad']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'