Summarizing Public Opinions in Tweets
Nibir Nayan Bora · 2012
The objective of Sentiment Analysis is to identify any clue of positive or negative emotions in a piece of text reflective of the authors opinions on a subject. When performed on large aggregations of user generated content, Sentiment Analysis may be helpful in extracting public opinions. We use Twitter for this purpose and build a classifier which classifies a set of tweets. Often, Machine Learning techniques are applied to Sentiment Classification, which requires a labeled training set of considerable size. We introduce the approach of using words with sentiment value as noisy label in a distant supervised learning environment. We created a training set of such Tweets and used it to train a Naive Bayes Classifier. We test the accuracy of our classifier using a hand labeled training set. Finally, we check if applying a combination of minimum word frequency threshold and Categorical Proportional Difference as the Feature Selection method enhances the accuracy.