A Hybrid Approach for Twitter Sentiment Analysis

This paper introduces an approach for automatically classifying the sentiment of Twitter messages. These messages are classified as either positive or negative. This is useful for consumers who want to extract the sentiment of product before purchase, or companies that want to monitor the public sentiment of their brand. In this paper, a three stage hierarchical model is proposed for sentiment extraction, first labeling with emoticons is done, then tweets are labeled using pre-defined lists of words with strong positive or negative sentiments and finally tokens are weighted based on subjectivity lexicon and proposed probability based method. Further, various cascading and hybrid methods are proposed based on subjectivity lexicon and Probability based method. In addition to this, effect of discourse relations is also investigated at the pre-processing step. Experimental results show the effectiveness of the proposed hybrid approach for sentiment classification of tweets.