Combining Lexicon and Machine Learning Method to Enhance the Accuracy of Sentiment Analysis on Big Data
During the day to day life, our opinions and opinions of others toward a product or event play a vital role in the decision making process. In recent times, the outburst of social media over the web has an abundant impact on an individual's and the organization's decision making 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. As more number of people lively connected to the twitter, more data are generated by the people which lead to the big data. 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. In addition the contextual valence shifter was used to evaluate the context sentiments and to solve the contextdependent problem. The combination of these two components will produce better accuracy.