Summarizing Public Opinions in Tweets

摘要

The objective of Sentiment Analysis is to identify any clue of positive or negative emotions in apiece 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.