Political Tendency Identification in Twitter Using Naive Bayes Classification

The generation of social media in the recent past has provided end users a powerful dias to voice their opinions. Businesses need to analyze the polarity of these opinions in order to understand user orientation and thereby make smarter judgement. One such application is in the field of politics, where political parties need to understand public opinion and thus determine their campaigning strategy. Sentiment analysis on social media data has been seen by many as an effective tool to monitor user choice and inclination. Naive Bayes and SVM are Supervised Learning Algorithms which require a training data set to achieve Sentiment analysis. The efficiency of these algorithms is contingent upon the quantity as well as the quality of the labeled training data.