Multiclass Sentiment Analysis with Restaurant Reviews Moontae Lee

Multiclass Sentiment Analysis with Restaurant Reviews Moontae Lee and Patrick Grafe

Open. Table. com Data Set • Overall Rating (1 to 5 stars) • • Food Rating (1 to 5 stars) Ambiance Rating (1 to 5 stars) Service Rating (1 to 5 stars) Noise Rating (1 to 3) • Data Set statistics • Heavily biased toward 5 star ratings

Strategies • • • Spell Correction POS Tagging Unigram/Bigram/Trigram Stop Words Pruning

Spell Correction Common Spelling Mistakes: • Restaurant: resturant, restuarant, restaurante • Waiter: waitor • Service: sevice, serivce Distance Metrics: • Edit Distance • Levenstein Distance • Keyboard Distance • Sound Distance

Parsing Problem Sentences: • The atmosphere is pretty bad and food is quite good • The food, service, and atmosphere were fantastic!

Results Training Set Accuracy 84. 62% Training Set Test Set MSE Accuracy 0. 3398 57. 36% 0. 8231 Unigram with spell check 84. 53% 0. 3256 57. 12% 0. 8297 Unigram/Bigram/Trigram 95. 65% 0. 1058 57. 18% 0. 9181 Unigram/Bigram/Trigram with pruning 98. 66% 0. 0321 56. 71% 0. 9052 Unigram/Bigram/Trigram with spell checking 95. 53% 0. 1088 57. 27% 0. 8984 Unigram/Bigram/Trigram with no 95. 38% stop words and spell check 0. 1110 57. 42% 0. 8936 Unigram/Bigram/Trigram with no 98. 77% stop words, pruning, and spell check 0. 0304 56. 56% 0. 8970 Unigram Test Set MSE

Conclusions • Inherently Difficult Data Set • More Advanced Techniques Necessary