Hybrid Based Approach to Enhance the Accuracy of Sentiment Analysis on Tweets
The opinion of others toward a product or event plays an important role in the decision making process. In recent times, the explosion of social media over the web has a rich impact on an individual's and the organization's decisionmaking process about certain content. Twitter which is a leading micro blogging website allows the people to post their opinions, state of mind, or status toward products or any events such as politics and sports. Sentiment analysis performs an essential role to extract the content of the tweets. This research work consists of two components: a lexicon builder and a sentiment classifier. The proposed hybrid based Senti_iLexi_iClassi approach is the combination of lexicon and machine learning based approaches. In sentiment analysis, the context dependent word is a major problem. To solve this problem, the existing contextual valence shifter (CVS) method is improved and used to assign polarity values using the proposed Context Polarity Measure (CPM) To find the polarity of context dependent text, the n grams model is used. The tweets are classified more accurately and produces better results. Keywords sentiment analysis, context dependent word, hybrid based approach, retweet count, support vector machines.