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CS571: Sentiment Analysis

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CS571: Natural Language Processing
https://github.com/emory-courses/cs571

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CS571: Sentiment Analysis

  1. 1. Sentiment Analysis Natural Language Processing Emory University
 Jinho D. Choi
  2. 2. Sentiment Analysis 2 A task of identifying the sentiment of a document. ↑ sentence, twit, blog, article etc. Identifying sentiments of certain aspects. Camera Lens Resolution Price sizeBrand
  3. 3. Reviews & Ratings 3 https://www.google.com/shopping http://ratemyprofessors.com
  4. 4. Movie Reviews 4 http://www.cs.cornell.edu/people/pabo/movie-review-data/ http://www.rottentomatoes.com the film provides some great insight into the neurotic mindset of all comics -- even those who have reached the absolute top of the game . Postive most of the problems with the film don't derive from the screenplay , but rather the mediocre performances by most of the actors involved Negative
  5. 5. Dictionary-based Approach 5 Create lists of positive/negative words (phrases). suck terrible awful unwatchable hideous Negative dazzling brilliant phenomenal excellent fantastic Positive Sentiment = |Positive words| - |Negative words| Around 65% accuracy!
  6. 6. Machine Learning Approach 6 N-gram models the film provides some great insight into the neurotic mindset of all comics -- even those who have reached the absolute top of the game . 1-gram = {the, film, provides, …} 2-gram = {the_film, file_provides, provides_some, …} 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 ↑ the ↑ film ↑ provides ↑
 the_film ↑ film_provides ↑ provides_some NB: 80.6 ME: 80.8 SVM: 82.7
  7. 7. Machine Learning Approach 7 N-gram models the film provides some great insight into the neurotic mindset of all comics -- even those who have reached the absolute top of the game . Stop-word 1-gram = {the, film, provides, some, …} 2-gram = {the_film, file_provides, provides_some, …} TF-IDF Term frequency Document frequency
  8. 8. Challenges 8 I liked this movie. I didn’t like this movie. Negation I liked this movie, but not the actors. Mixture Besides a few bad plots, clumsy graphics, 
 and lousy acting, I liked this movie Thwart This movie is unreal. Ambiguity Parse Tree? Aspect-based?

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