Input: ['The wine the service was very good too .']

Output: [['wine', 'drinks quality', 'positive', 'good'], ['service', 'service 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: ["It 's cuz it 's so good you need to taste it ! ! !"]

Output: [['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: ["Open late ( well as late as I ever got there and I 'm a night person )"]

Output: [['NULL', 'restaurant miscellaneous', 'positive', '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: ['The only problem is that the manager is a complete incompetent .']

Output: [['manager', 'service general', 'negative', 'incompetent']]

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: ['Mine was a little burnt but still delicious with goat cheese and panchetta ( raddichio was kind of bitter though ) .']

Output: [['raddichio', 'food quality', 'negative', 'bitter'], ['NULL', '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: ['The service is awful .']

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

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 has the the correct ambience and an excellent staff to make you feel like a guest and a friend at the same time .']

Output: [['ambience', 'ambience general', 'positive', 'correct'], ['staff', 'service general', 'positive', 'excellent']]

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: ['Calling the place Hampton Chutney Co. does warn you that these folks offer more style than substance , but in this unattractive room with unhelpful clerks there was a dearth of the former too .']

Output: [['place', 'restaurant general', 'negative', 'unattractive'], ['room', 'ambience general', 'negative', 'unattractive'], ['clerks', 'service general', 'negative', 'unhelpful']]

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: ['Probably would not go again ...']

Output: [['NULL', 'restaurant general', 'negative', 'not go 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: ['My friend devoured her chicken and mashed potatos .']

Output: [['chicken and mashed potatos', 'food quality', 'positive', 'devoured']]

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 melted in my little mouth and the perfect consistency-not too fishy , creamy , and slightly buttery .']

Output: [['NULL', 'food quality', 'positive', 'perfect consistency'], ['NULL', 'food quality', 'positive', 'not too fishy'], ['NULL', 'food quality', 'positive', 'creamy'], ['NULL', 'food quality', 'positive', 'buttery']]

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 then shooed inside .']

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

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: ['Although we were looking for regular lettuce and some walnuts the salads we got were great .']

Output: [['salads', '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: ['Great bagels made the old-fashioned way .']

Output: [['bagels', 'food quality', 'positive', 'Great'], ['bagels', 'food style_options', '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: ["It 's boring on the inside , and our sushi was pretty below average ... the tuna was soggy and the other rolls had no flavor ."]

Output: [['NULL', 'ambience general', 'negative', 'boring'], ['sushi', 'food quality', 'negative', 'below average'], ['tuna', 'food quality', 'negative', 'soggy'], ['rolls', 'food quality', 'negative', 'no flavor']]

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 seated outside and the waiter spilled red wine and hot tea on myself and my date .']

Output: [['waiter', 'service general', 'negative', 'spilled']]

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 is fast too .']

Output: [['Delivery', '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: ['My friend got the mushroom pizza which tasted better .']

Output: [['mushroom pizza', 'food quality', 'positive', 'better']]

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: ['Go to Volare for 1st class service and terrific food .']

Output: [['service', 'service general', 'positive', '1st class'], ['food', 'food quality', 'positive', 'terrific']]

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 found the food to be outstanding , particulary the salmon dish I had .']

Output: [['food', 'food quality', 'positive', 'outstanding'], ['salmon dish', 'food quality', 'positive', 'outstanding']]

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 outdoor atmosphere of sitting on the sidewalk watching the world go by 50 feet away on 6th avenue on a cool evening was wonderful .']

Output: [['outdoor atmosphere', 'location general', 'positive', 'wonderful']]

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 friend from Milan and myself were pleasantly surprised when we arrived and everyone spoke italian .']

Output: [['NULL', 'restaurant miscellaneous', '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: ["If you 've ever been along the river in Weehawken you have an idea of the top of view the chart house has to offer ."]

Output: [['view', 'location general', 'positive', 'the top of view the chart house has to offer']]

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: ['Try the sea bass .']

Output: [['sea bass', 'food quality', 'positive', 'Try']]

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 family came in he gave them apps to test their palets , and then ordered for them .']

Output: [['NULL', 'service general', 'positive', 'ordered for them']]

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: ['Go hungry and enjoy .']

Output: [['NULL', 'restaurant general', '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: ['the waitstaffs are nice though .']

Output: [['waitstaffs', '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: ['The food was not fresh , the sauces were bland and very oily .']

Output: [['food', 'food quality', 'negative', 'not fresh'], ['sauces', 'food quality', 'negative', 'bland'], ['sauces', 'food quality', 'negative', 'oily']]

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: ['Although they do the typical what kind of water would you like questions the service was good and overall very relaxing to place to eat .']

Output: [['service', 'service general', 'positive', 'good'], ['place', 'ambience general', 'positive', 'relaxing']]

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 asparagus , truffle oil , parmesan bruschetta is a winner ! )']

Output: [['asparagus , truffle oil , parmesan bruschetta', 'food quality', 'positive', 'winner']]

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 pizza and fantastic service .']

Output: [['pizza', 'food quality', 'positive', 'Great'], ['service', 'service general', 'positive', 'fantastic']]

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 a Japanese native , I 've lived in the Tristate area for over 8 years , but I was just so amazed at this place ."]

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

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 must try the shrimp appetizers .']

Output: [['shrimp appetizers', 'food quality', 'positive', 'try']]

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 recommend Roxy 's for that , but not for their food ."]

Output: [['food', 'food quality', 'negative', 'recommend'], ['NULL', 'food quality', 'negative', 'not for their food']]

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 pizza was pretty good and huge .']

Output: [['pizza', 'food quality', 'positive', 'good'], ['pizza', 'food style_options', 'positive', 'huge']]

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 workers there also absolutely load the bagel with cream cheese ( gets a little messy ) .']

Output: [['bagel', 'food style_options', 'negative', 'messy']]

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 little place has a cute interior decor and affordable city prices .']

Output: [['interior decor', 'ambience general', 'positive', 'cute'], ['place', 'restaurant prices', 'positive', 'little']]

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 come from a family of pizzeria owners , and I 'm almost ashamed to say that the pizza in Fornino 's blows my families receipies away ."]

Output: [['pizza', 'food quality', 'positive', 'ashamed']]

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 porcini mushroom pasta special was tasteless , so was the seafood tagliatelle .']

Output: [['porcini mushroom pasta special', 'food quality', 'negative', 'tasteless'], ['seafood tagliatelle', 'food quality', 'negative', 'tasteless']]

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 okay and the prices here are mediocre .']

Output: [['food', 'food quality', 'neutral', 'okay'], ['NULL', 'restaurant prices', 'neutral', 'mediocre']]

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: ['Slightly on the pricey side but worth it !']

Output: [['NULL', 'restaurant prices', 'negative', 'pricey'], ['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: ['I had the best ravioli ever .']

Output: [['ravioli', '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: ['It was pretty inexpensive too .']

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

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 will not be disapointed at all .']

Output: [['NULL', 'restaurant general', 'positive', 'will not be disapointed']]

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 pad penang is delicious and everything else is fantastic .']

Output: [['pad penang', 'food quality', 'positive', 'delicious'], ['NULL', 'food quality', 'positive', 'fantastic']]

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 ingredients and everything is made to order .']

Output: [['ingredients', '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: ["also it 's great to have dinner in a very romantic and comfortable place , the service it 's just perfect ... they 're so frendly that we never want to live the place !"]

Output: [['place', 'ambience general', 'positive', 'romantic'], ['place', 'ambience general', 'positive', 'comfortable'], ['service', 'service general', 'positive', 'perfect']]

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 dosas are skimpy , unattractive and drip with grease , and personally I 'd drink popcorn topping before I 'd eat another one of these ."]

Output: [['dosas', 'food style_options', 'negative', 'skimpy'], ['dosas', 'food quality', 'negative', 'unattractive']]

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 last time i walked by it looked pretty empty . hmmm .']

Output: [['NULL', 'restaurant miscellaneous', 'negative', 'empty']]

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 bar is very well stocked with interesting beers and well priced wines .']

Output: [['bar', 'drinks style_options', 'positive', 'well stocked'], ['beers', 'drinks style_options', 'positive', 'interesting'], ['wines', 'drinks prices', 'positive', 'well 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: ['After all that , they complained to me about the small tip .']

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

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: ['Very affordable and excellent ambient !']

Output: [['ambient', 'ambience general', 'positive', 'excellent'], ['NULL', 'restaurant prices', 'positive', 'affordable']]

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 mussles were the fishiest things I 've ever tasted , the seabass was bland , the goat cheese salad was missing the goat cheese , the penne w/ chicken had bones in it ... It was disgusting ."]

Output: [['mussles', 'food quality', 'negative', 'fishiest'], ['seabass', 'food quality', 'negative', 'bland'], ['goat cheese salad', 'food quality', 'negative', 'missing'], ['penne w/ chicken', 'food quality', 'negative', 'disgusting']]

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 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: ['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: ["They forgot a sandwich , did n't include plastic forks , and did n't include pita with the hummus platter ."]

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

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 was prompt , friendly and great .']

Output: [['Service', 'service general', 'positive', 'prompt'], ['Service', 'service general', 'positive', 'friendly'], ['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: ["This was a repeat visit and we 'll definitely be back again ."]

Output: [['NULL', 'restaurant general', 'positive', 'be back 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: ['We , there were four of us , arrived at noon - the place was empty - and the staff acted like we were imposing on them and they were very rude .']

Output: [['staff', '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: ['sometimes i get good food and ok service .']

Output: [['food', 'food quality', 'positive', 'good'], ['service', 'service general', 'neutral', 'ok']]

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 friendly and attentive .']

Output: [['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: ['My fav was the sassy lassi ...']

Output: [['sassy lassi', 'drinks quality', 'positive', 'fav']]

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 always packed .']

Output: [['place', 'ambience general', 'neutral', 'packed']]

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 do n't think I would go again ."]

Output: [['NULL', 'restaurant general', 'negative', "do n't think I would go 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 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: ['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: ["Everytime I decide to try another place on the UES , I get angry that I did n't just go to Zucchero Pomodori ."]

Output: [['Zucchero Pomodori', 'restaurant general', 'positive', "did n't just go to"]]

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: ['After dinner the manager grabbed my boyfriend , asked him : Where are you from ... maybe you dont know how things work in America ... and in the end stormed away almost teareyed yelling that tips are the only thing they survive on .']

Output: [['manager', 'service general', 'negative', 'yelling']]

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 filet mignon dish was superb !']

Output: [['filet mignon dish', 'food quality', 'positive', 'superb']]

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 was prompt and courteous .']

Output: [['Service', 'service general', 'positive', 'prompt'], ['Service', 'service general', 'positive', 'courteous']]

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 still mad that i had to pay for lousy food ."]

Output: [['food', 'food quality', 'negative', 'lousy']]

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 lobster sandwich is good and the spaghetti with Scallops and Shrimp is great .']

Output: [['lobster sandwich', 'food quality', 'positive', 'good'], ['spaghetti with Scallops and Shrimp', '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: ['Patroon features a nice cigar bar and has great staff .']

Output: [['cigar bar', 'ambience general', 'positive', 'nice'], ['staff', '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: ['There is no excuse for such lousy service !']

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

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: ['The food was absolutely amazing ! !']

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

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: ['Overall I would recommend it and go back again .']

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: ['This is an amazing place to try some roti rolls .']

Output: [['roti rolls', 'food quality', 'positive', 'try']]

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 love the food , drinks , and atmosphere !']

Output: [['food', 'food quality', 'positive', 'love'], ['drinks', 'drinks quality', 'positive', 'love'], ['atmosphere', 'ambience 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: ['Hats off to the chef .']

Output: [['chef', 'food quality', 'positive', 'Hats off']]

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: ["Okay-i do n't mind the oily part ( cause most are cooked that way ) but it was way too bland ."]

Output: [['NULL', 'food quality', 'negative', 'oily'], ['NULL', 'food quality', 'negative', 'bland']]

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 by far my favorite place in the neighborhood .']

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 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: ["There is a downside if you 're ordering in -- the delivery guys have MAJOR attitude ."]

Output: [['delivery guys', 'service general', 'negative', 'downside']]

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 're rude at times , and not very friendly ."]

Output: [['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: ['We were less than impressed .']

Output: [['NULL', 'restaurant general', 'negative', 'less than impressed']]

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 charge different prices all the time .']

Output: [['NULL', 'service general', 'negative', 'charge different prices']]

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: ['Waitstaff are very friendly .']

Output: [['Waitstaff', 'service general', 'positive', 'friendly']]

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 not helpful in the least and will give you the grand run around so by the time the event date rolls around you will not only regret chosing this place , but also become hostile !']

Output: [['NULL', 'service general', 'negative', 'not helpful in the least']]

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 to say I have never had a disapointing meal here .']

Output: [['meal', 'food quality', 'positive', 'never had a disapointing']]

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 attentive , yet discreet .']

Output: [['service', 'service general', 'positive', 'attentive'], ['service', 'service general', 'positive', 'discreet']]

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 classic !']

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

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 waitress moved our table practically into the bathroom and when we asked to cancel our dinner orders because we did not want to eat sitting on the toilet , we were told no ...']

Output: [['waitress', 'service general', 'negative', 'sitting on the toilet']]

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 it all comes at a very reasonable price ( congee , noodles , and rice dishes are no more than $ 3-6 each ) .']

Output: [['NULL', 'food prices', 'positive', 'reasonable'], ['congee', 'food prices', 'positive', 'no more than $ 3-6 each'], ['noodles', 'food prices', 'positive', 'no more than $ 3-6 each'], ['rice dishes', 'food prices', 'positive', 'no more than $ 3-6 each']]

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: ['Make sure you have the Spicy Scallop roll ...']

Output: [['Spicy Scallop roll', 'food quality', 'positive', 'Make sure']]

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 , if you want great food at a great price and do n't mind the decor , you ca n't beat this place ."]

Output: [['food', 'food quality', 'positive', 'great'], ['food', 'food prices', '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: ['During the course of the past 3 months , the chef and staff changed and it was not for the better .']

Output: [['chef', 'food quality', 'negative', 'changed'], ['staff', 'service general', 'negative', 'changed']]

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 happen to have a policy that goes along with a little bit of self-respect , which includes not letting a waiter intimidate me , i.e . make me feel bad asking for trivialities like water , or the check .']

Output: [['waiter', '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']]'


Input: ['I got an excellent piece of cheesecake and we had several other nice pastries .']

Output: [['cheesecake', 'food quality', 'positive', 'excellent'], ['pastries', 'food quality', '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: ['The service was friendly and the atmosphere was casual .']

Output: [['service', 'service general', 'positive', 'friendly'], ['atmosphere', 'ambience general', 'neutral', 'casual']]

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: ['Definitely a great spot for a nice occasion or date .']

Output: [['spot', '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: ['Staff is very accomodating .']

Output: [['Staff', 'service general', 'positive', 'accomodating']]

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: ["Rao 's has the best service and atmosphere in NYC ."]

Output: [['service', 'service general', 'positive', 'best'], ['atmosphere', 'ambience 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: ["Consequently , their burgers fell apart in their hands and made such a mess that they did'nt feel like finishing them ."]

Output: [['burgers', 'food style_options', 'negative', 'fell apart']]

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 recommend it !']

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: ['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 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: ['Try the congee and the donut like deep fried dough they call Ow Ley Soh , a delicious and sweet tasting bread .']

Output: [['congee', 'food quality', 'positive', 'Try'], ['Ow Ley Soh', 'food quality', 'positive', 'delicious'], ['Ow Ley Soh', 'food quality', 'positive', 'sweet']]

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 THIS PLACE !']

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: ["Volare virgins or weekly regulars , everyone gets treated the same and you ca n't ask for more than that when the service is this friendly ."]

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

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: ['Saul is the best restaurant on Smith Street and in Brooklyn .']

Output: [['Saul', '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: ['Fish was overdone .']

Output: [['Fish', 'food quality', 'negative', 'overdone']]

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: ["Excellent spot for holiday get togethers with co-workers or friends that you have n't seen in a while ."]

Output: [['spot', 'restaurant miscellaneous', 'positive', 'Excellent']]

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']]'