Input: ['The mussels were fantastic and so was the dessert ... definitely going to be back very soon .']

Output: [['mussels', 'food quality', 'positive', 'fantastic'], ['dessert', 'food quality', 'positive', 'fantastic'], ['NULL', 'restaurant general', 'positive', 'going to be 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: ['Slightly above average wines start at $ 70+ with only one selection listed at $ 30+ .']

Output: [['wines', 'drinks quality', 'negative', 'above average'], ['wines', 'drinks prices', 'negative', 'above average']]

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


Input: ['There was no tap beer that evening , which was a disappointment .']

Output: [['beer', 'drinks style_options', 'negative', 'disappointment']]

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 has got to be one of the most overrated restaurants in Brooklyn .']

Output: [['NULL', 'restaurant general', 'negative', 'overrated']]

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: ['Love Al Di La']

Output: [['Al Di La', '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: ['Yes , the place is classy and beautiful , but they most certainly target the uber whealthy Not the common joe that wants to go all out every once in a while .']

Output: [['place', 'ambience general', 'positive', 'classy'], ['place', 'ambience general', 'positive', 'beautiful'], ['place', 'restaurant prices', 'negative', 'target the uber whealthy']]

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 baked clams octopus we shared as appetizers were the best we 've ever had ! !"]

Output: [['baked clams octopus', '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: ['The floor was wet , the trash can filled with hand towels n all over the floor , no soap , and no hand towels left .']

Output: [['NULL', 'ambience general', 'negative', 'no hand towels left']]

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 we were to move from the upper east side , we would genuinely miss this restaurant .']

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

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: ['Pizza - the only pizza in NYC that should not have additional toppings - the crust tastes like the best , freshly baked bread !']

Output: [['crust', 'food quality', 'positive', 'best'], ['pizza', 'food quality', 'positive', 'should not have additional toppings']]

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


Input: ['Best Italian food I ever had ( and being Italian , that means alot ) .']

Output: [['Italian food', '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: ['I LOOOVE their eggplant pizza , as well as their pastas !']

Output: [['eggplant pizza', 'food quality', 'positive', 'LOOOVE'], ['pastas', 'food quality', 'positive', 'LOOOVE']]

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


Input: ['Who has room for Cheesesticks with the best pizza in NYC !']

Output: [['pizza', '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: ['The characters really make for an enjoyable experience .']

Output: [['characters', 'ambience general', 'positive', 'enjoyable']]

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 looked like shredded cheese partly done - still in strips .']

Output: [['NULL', 'food quality', 'negative', 'shredded cheese partly done']]

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


Input: ["Over the years the host , Vittorio , and his crew , have always treated me as family -- although with all the business this not-so-little gem does , it amazing he 's even able to remember a consistent but not-so-frequent visitor ."]

Output: [['host', 'service general', 'positive', 'amazing'], ['crew', 'service general', 'positive', 'treated me as family']]

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 quantity is also very good , you will come out satisfied .']

Output: [['quantity', 'food style_options', 'positive', 'good'], ['quantity', 'food style_options', 'positive', 'satisfied']]

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: ['Went there with my wife and we had to wait for a table even though you could see there many that were empty with not reservation sigh on them .']

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

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: ['On the way out , we heard of other guests complaining about similar issues .']

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

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 one of the best comfort food places in the city .']

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

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


Input: ['We were ushered to the bar to wait momentarily and upon arrival were so excited .']

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

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 quick .']

Output: [['Service', 'service general', 'positive', 'quick']]

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: ["its a little out of the way if you do n't live in the neighborhood , but definitely worth the trip from wherever you are ."]

Output: [['NULL', 'location general', 'negative', 'a little out of the way'], ['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: ['A coworker and I tried Pacifico after work a few Fridays and loved it .']

Output: [['Pacifico', 'restaurant general', 'positive', 'loved']]

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


Input: ['The waitress was not attentive at all .']

Output: [['waitress', 'service general', 'negative', 'not 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: ["If you do n't mind pre-sliced low quality fish , unfriendly staff and a sushi chef that looks like he is miserable then this is your place ."]

Output: [['fish', 'food quality', 'negative', 'low quality'], ['staff', 'service general', 'negative', 'unfriendly'], ['sushi chef', 'restaurant miscellaneous', 'negative', 'miserable']]

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: ['Furthermore , the rice had no seasoning , so the sushi was bland and disgusting .']

Output: [['rice', 'food quality', 'negative', 'no seasoning'], ['sushi', 'food quality', 'negative', 'bland'], ['sushi', '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: ['The first time I went , and was completely taken by the live jazz band and atmosphere , I ordered the Lobster Cobb Salad .']

Output: [['live jazz band', 'ambience general', 'positive', 'taken'], ['atmosphere', 'ambience general', 'positive', 'taken']]

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 their eggs benedict for brunch , which were the worst in my entire life , I tried removing the hollondaise sauce completely that was how failed it was .']

Output: [['eggs benedict', 'food quality', 'negative', 'worst']]

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: ['Kind of a small place but I guess if they are not too busy might be able to fit a group or kids .']

Output: [['place', 'restaurant miscellaneous', 'neutral', 'small']]

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 Girlfriend and I stumbled onto this hopping place the other night and had a great time !']

Output: [['place', 'restaurant general', 'positive', 'hopping'], ['place', 'restaurant general', 'positive', 'great time']]

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 hot dogs are top notch , and they 're Slamwich is amazing !"]

Output: [['hot dogs', 'food quality', 'positive', 'top notch'], ['Slamwich', '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: ['skip dessert .']

Output: [['dessert', 'food quality', 'negative', 'skip']]

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 quick and friendly .']

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

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: ['Chow fun was dry ; pork shu mai was more than usually greasy and had to share a table with loud and rude family .']

Output: [['Chow fun', 'food quality', 'negative', 'dry'], ['pork shu mai', 'food quality', 'negative', 'greasy'], ['NULL', 'ambience general', 'negative', 'loud'], ['NULL', 'ambience 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: ["Yes , they use fancy ingredients , but even fancy ingredients do n't make for good pizza unless someone knows how to get the crust right ."]

Output: [['ingredients', 'food quality', 'positive', 'fancy'], ['pizza', 'food quality', 'negative', "do n't make for good pizza"]]

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: ['Authentic Pakistani food .']

Output: [['Pakistani food', 'food quality', 'positive', 'Authentic']]

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


Input: ['The food was actually aweful .']

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

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: ['This restaurant was way overhyped .']

Output: [['restaurant', 'restaurant general', 'negative', 'overhyped']]

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


Input: ['Best restaurant in Brooklyn']

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

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


Input: ['Service was slow , but the people were friendly .']

Output: [['Service', 'service general', 'negative', 'slow'], ['people', '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: ['However , I think Jeckll and Hydes t is one of those places that is fun to do once .']

Output: [['Jeckll and Hydes', 'restaurant general', 'positive', 'fun']]

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


Input: ['The fish was really , really fresh .']

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

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


Input: ['I had a grat time at Jekyll and Hyde !']

Output: [['Jekyll and Hyde', 'restaurant general', 'positive', 'had a grat time']]

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: ["On that scale , it 's a world-beater ."]

Output: [['NULL', 'restaurant general', 'negative', 'world-beater']]

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 drinks are amazing and half off till 8pm .']

Output: [['drinks', 'drinks quality', 'positive', 'amazing'], ['drinks', 'drinks prices', '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: ["I 've waited over one hour for food ."]

Output: [['NULL', 'service general', 'negative', 'waited over one hour']]

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 two times I ordered from here my food was soo spicy that I could barely eat it , and the spice took away from the flavor of the dish .']

Output: [['food', 'food quality', 'negative', 'spicy'], ['spice', 'food quality', 'negative', 'could barely eat it']]

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


Input: ['Haru on Park S is simply disgusting .']

Output: [['Haru on Park S', 'restaurant general', '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: ["those rolls were big , but not good and sashimi was n't fresh ."]

Output: [['rolls', 'food style_options', 'positive', 'big'], ['rolls', 'food quality', 'negative', 'not good'], ['sashimi', 'food quality', 'negative', "was n't 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: ['This place is really trendi but they have forgotten about the most important part of a restaurant , the food .']

Output: [['food', 'food quality', 'negative', 'forgotten'], ['place', 'ambience general', 'positive', 'trendi']]

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: ['Pacifico is a great place to casually hang out .']

Output: [['Pacifico', '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: ['We were not dissappointed in the least bit by this little gem .']

Output: [['NULL', 'restaurant general', 'positive', 'not dissappointed in the least bit']]

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 attended a holiday dinner at the restaurant , and the food was majorly disappointing .']

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

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 should pass on the calamari .']

Output: [['calamari', 'food quality', 'negative', 'pass']]

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: ['His response was smug , arrogant , and condescending , totally consistent with his deportment on display all evening .']

Output: [['NULL', 'service general', 'negative', 'smug'], ['NULL', 'service general', 'negative', 'arrogant'], ['NULL', 'service general', 'negative', 'condescending']]

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: ['The only thing more wonderful than the food ( which is exceptional ) is the service .']

Output: [['food', 'food quality', 'positive', 'exceptional'], ['service', 'service 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: ["short and sweet – seating is great : it 's romantic , cozy and private ."]

Output: [['seating', 'ambience general', 'positive', 'great']]

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


Input: ['She was very helpful in suggesting us drinks and helped us in ordering a lot of good dishes since we knew nothing about Indian food .']

Output: [['NULL', 'service general', 'positive', 'helpful'], ['dishes', '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: ['The rolls are creative and I have yet to find another sushi place that serves up more inventive yet delicious japanese food .']

Output: [['rolls', 'food style_options', 'positive', 'creative'], ['japanese food', 'food style_options', 'positive', 'inventive'], ['japanese 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: ["I have it a 4 instead of 5 because of the price ( just chicken tikka masala - no bread of rice - is $ 25 ) , which I would expect at a upscale Indian restaurant but this place doesn 't have an upscale feel ."]

Output: [['place', 'restaurant general', 'positive', '4 instead of 5'], ['chicken tikka masala', 'food prices', 'negative', 'no bread of rice'], ['feel', 'ambience general', 'negative', "doesn 't have an upscale feel"]]

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 is overpriced and soggy .']

Output: [['pizza', 'food quality', 'negative', 'soggy'], ['pizza', 'food prices', 'negative', 'overpriced']]

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 looked very appetizing and delicious since it came on a variety of fancy plates .']

Output: [['food', 'food style_options', 'positive', 'appetizing'], ['food', 'food style_options', '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: ["All in all we 're already coming up with excuses to go ahead really soon in the next few wks ! ! ! !"]

Output: [['NULL', 'restaurant general', 'positive', 'go ahead really soon']]

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: ['Hands down the best pizza on the planet .']

Output: [['pizza', '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: ['never swaying , never a bad meal , never bad service ...']

Output: [['NULL', 'restaurant general', 'positive', 'never swaying'], ['meal', 'food quality', 'positive', 'never a bad'], ['service', 'service general', 'positive', 'never 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: ['The martinis are amazing and very fairly priced .']

Output: [['martinis', 'drinks quality', 'positive', 'amazing'], ['martinis', 'drinks prices', 'positive', 'fairly 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: ['Despite the confusing mirrors this will likely be my go-to for modern Japanese food for the foreseeable future .']

Output: [['modern Japanese food', 'food quality', 'positive', 'go-to for'], ['mirrors', 'ambience general', 'negative', 'confusing']]

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: ['Avoid this place !']

Output: [['place', 'restaurant general', 'negative', 'Avoid']]

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: ['Quite frankly , this is some of the worst sushi I have ever tried .']

Output: [['sushi', 'food quality', 'negative', 'worst']]

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: ['Such a disappointment ...']

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

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 was one of the people that went for this horrible experience .']

Output: [['NULL', 'restaurant general', 'negative', 'horrible']]

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 worth the trip to Battery Park City !']

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

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


Input: ['I would definitely recommend SEA if you like thai cuisine !']

Output: [['thai cuisine', 'food quality', 'positive', 'like']]

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 eaten at Saul , many times , the food is always consistently , outrageously good .']

Output: [['food', '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: ['Big Wong gets big Ups for a fine establishment .']

Output: [['Big Wong', 'restaurant general', 'positive', 'big Ups'], ['Big Wong', 'restaurant general', 'positive', 'fine']]

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


Input: ['The lamb was tender so full of flavor , the dessert was divine ! !']

Output: [['lamb', 'food quality', 'positive', 'tender'], ['dessert', 'food quality', 'positive', 'divine']]

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 and wine were excellent -- the service too -- but what really MADE this place was the backyard dining area .']

Output: [['Pizza', 'food quality', 'positive', 'excellent'], ['wine', 'drinks quality', 'positive', 'excellent'], ['service', '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: ['Only complaint is the pricing -- I believe it would be more reasonable to pay a dollar less on each item listed on the menu .']

Output: [['menu', 'food prices', 'negative', 'complaint']]

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 level of rudeness was preposterous .']

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

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 also very good .']

Output: [['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: ['Try the lobster teriyaki and the rose special roll .']

Output: [['lobster teriyaki', 'food quality', 'positive', 'Try'], ['rose special roll', '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: ['Try everything for that matter , it is all good .']

Output: [['NULL', 'food quality', 'positive', 'all 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: ['Try it !']

Output: [['NULL', 'restaurant general', '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: ['They have it all -- great price , food , and service .']

Output: [['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'great'], ['service', 'service general', 'positive', 'great']]

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


Input: ['The blond wood decor is very soothing , the premium sake is excellent and the service is great .']

Output: [['blond wood decor', 'ambience general', 'positive', 'soothing'], ['premium sake', 'drinks quality', 'positive', 'soothing'], ['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 place is great .']

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

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


Input: ["I 'll be back for sure ."]

Output: [['NULL', 'restaurant general', 'positive', 'be 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: ['Everything we had was good or ok ... but definitely nothing great .']

Output: [['NULL', 'food quality', 'neutral', 'nothing 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: ['Again , no apology , no is there anything else I can get you , no can I get you a drink to make up for it , nothing ! ! ! !']

Output: [['NULL', 'service general', 'negative', 'no apology']]

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: ["Watch the talented belly dancers as you enjoy delicious baba ganoush that 's more lemony than smoky ."]

Output: [['baba ganoush', 'food quality', 'positive', 'enjoy delicious'], ['belly dancers', 'ambience general', 'positive', 'talented']]

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 devine , oysters where a sensual as they come , and the price ca n't be beat ! ! !"]

Output: [['Service', 'service general', 'positive', 'devine'], ['oysters', 'food quality', 'positive', 'sensual'], ['NULL', 'restaurant prices', 'positive', "can't be beat"]]

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: ['Wonderful at holiday time .']

Output: [['NULL', 'restaurant miscellaneous', '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: ['It is terrific , as is the value .']

Output: [['NULL', 'food quality', 'positive', 'terrific'], ['NULL', 'food prices', '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: ['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: ['but overall i give it a 10']

Output: [['NULL', 'restaurant general', 'positive', 'give it a 10']]

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 go and eat out at many different restaurants and this is one place you have go and try .']

Output: [['place', 'restaurant general', 'positive', 'have go and 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: ["Friendly staff that actually lets you enjoy your meal and the company you 're with ."]

Output: [['staff', '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: ["It 's great to go for a quick lunch either alone or with a friend ."]

Output: [['NULL', '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: ['Was surprisingly disappointed .']

Output: [['NULL', 'food quality', 'negative', 'surprisingly disappointed']]

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: ['Zero ambiance to boot .']

Output: [['ambiance', 'ambience general', 'negative', 'Zero']]

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 sake menu should not be overlooked !']

Output: [['sake menu', 'drinks style_options', 'positive', 'overlooked']]

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 `` normal Indian food `` , but done really well .']

Output: [['Indian food', 'food style_options', 'neutral', 'normal'], ['Indian food', 'food quality', '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: ["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: ['but the service was a bit slow .']

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

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


Input: ['Best of all is the warm vibe , the owner is super friendly and service is fast .']

Output: [['vibe', 'ambience general', 'positive', 'warm'], ['owner', 'service general', 'positive', 'friendly'], ['service', 'service general', 'positive', 'fast']]

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


Input: ['The main entree was also very good .']

Output: [['main entree', '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: ["Also , I personally was n't a fan of the portobello and asparagus mole ."]

Output: [['portobello and asparagus mole', 'food quality', 'negative', 'fan']]

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 doesn 't look appetizing as it 's covered in squid ink and it turns your lips and teeth black , but the taste was phenomenal ."]

Output: [['NULL', 'food style_options', 'negative', "doesn 't look appetizing"], ['NULL', 'food quality', 'positive', 'phenomenal']]

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


Input: ['I really like both the scallops and the mahi mahi ( on saffron risotto yum ! ) .']

Output: [['scallops', 'food quality', 'positive', 'like'], ['mahi mahi ( on saffron risotto', 'food quality', 'positive', 'like']]

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 their chef 's specials -- they are to die for ."]

Output: [["chef 's specials", 'food quality', 'positive', 'Try'], ["chef 's specials", 'food quality', 'positive', 'die for']]

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


Input: ['It was the first place we ate on our first trip to New York , and it will be the last place we stop as we head out of town on our next trip to New York .']

Output: [['place', 'restaurant miscellaneous', 'positive', 'the last place we stop']]

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: ['Personal pans are the perfect size for those hungry nights .']

Output: [['Personal pans', 'food style_options', '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: ['IT WAS HORRIBLE .']

Output: [['NULL', 'restaurant general', 'negative', 'HORRIBLE']]

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']]'