Sentiment Analysis of Twitter

• To determine the polarity of a tweet, based on it’s content.

• Determining sentiment of a tweet using Naive Bayes algorithm, improving its efficiency by enhancing the data set with constraints, applied on the number of words used as features.

• Use of Base Line method to increase further efficiency.

Naive Bayes Algorithm: In this method we have several variations in the training the Naïve Bayes Classifier.

• Phase I:

In this method of finding sentiment we used the set 5333 and 5332 positive and negative movie reviews provided by Cornell University. We used three-fourth of the data set for training and one-fourth for testing.

• Phase II :

In this method we have used same data set but trained the classifier with most frequent 10,000 Features based on their Chi-Square Score Χ^2(t,c) =( N∗(AD−CB)^2) / [(A+C)∗(B+D)∗(A+B)∗(C+D)]

• Phase III:

In this we trained the classifier with 10000 features extracted from the movie reviews and tested on Twitter data collected from http://inclass.kaggle.com/c/si650winter11/data

• Phase IV:

In this we trained and tested with the Twitter data set previously collected. We used three-fourth for training and one-fourth for testing purposes.

Baseline model:

• In this method of calculating sentiment of the tweets we used bag of positive and negative words of size 2400+ and 4000+ respectively.

*We extracted the opinion features from the tweet text and added +1 if the word is in positive bag of words and -1 if it is negative bag of words.

• Special feature: We added positive and negative emoticons as they play a major role in determining sentiment expressed by the people.