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CS571: Sentiment Analysis
Sentiment analysis is a natural language processing task that identifies the sentiment of a document, such as whether a sentence, tweet, blog, or article expresses a positive or negative opinion. Machine learning approaches like n-gram models represent documents as collections of n-grams and use them to train classifiers like Naive Bayes, Maximum Entropy, and Support Vector Machines that can identify sentiment with over 80% accuracy. However, sentiment analysis still faces challenges in handling negation, mixtures of sentiment, ambiguity, and identifying sentiment towards specific aspects.
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CS571: Sentiment Analysis
- 1. Sentiment Analysis Natural LanguageProcessing Emory University Jinho D. Choi
- 2. Sentiment Analysis 2 A taskof identifying the sentiment of a document. ↑ sentence, twit, blog, article etc. Identifying sentiments of certain aspects. Camera Lens Resolution Price sizeBrand
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- 4. Movie Reviews 4 http://www.cs.cornell.edu/people/pabo/movie-review-data/ http://www.rottentomatoes.com the filmprovides 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. Dictionary-based Approach 5 Create listsof 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. Machine Learning Approach 6 N-grammodels 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. Machine Learning Approach 7 N-grammodels 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. Challenges 8 I liked thismovie. 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?