Predictive Approach toward Sentiments Analysis of Twitter Feeds using Maximum Entropy Classifier Method
Keywords:
Sentiment Analysis, Tweets, Social Networking, Naive Bayes Classifier, Maximum Entropy ClassifierAbstract
Twitter is most popular social networking platform where users produce and interact with messages called “tweets”. This serves as a medium for users to convey their feelings and thoughts about various subject. In this paper, an attempt is presented to conduct sentiment analysis on “tweets” using Maximum Entropy classifier. Maximum Entropy Classifier model is based on the principle of Maximum Entropy. The main purpose behind it is to choose the most uniform probabilistic model that maximizes the entropy, with given constraints. It attempts to classify the polarity of the tweet where it is either positive or negative. If the tweet has both positive and negative elements, the more dominant sentiment should be picked as the final label. In general, Maximum Entropy Classifier performs better than other Classifier as presented in result.
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