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.

1. Sentiment Analysis
Natural Language Processing
Emory University
Jinho D. Choi

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. Reviews & Ratings
3
https://www.google.com/shopping
http://ratemyprofessors.com

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. 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. 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. 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. 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?