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Enhancing Multiclass Sentiment Analysis for Restaurant Reviews Using OpenTable Data

Explore sentiment analysis strategies such as spell correction, POS tagging, and n-gram analysis on a restaurant review dataset. Discover how to handle biased ratings and common spelling mistakes for improved results and conclusions.

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Enhancing Multiclass Sentiment Analysis for Restaurant Reviews Using OpenTable Data

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  1. Multiclass Sentiment Analysis with Restaurant Reviews Moontae Lee and Patrick Grafe

  2. OpenTable.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

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

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

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

  6. Results

  7. Conclusions • Inherently Difficult Data Set • More Advanced Techniques Necessary

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