Application of machine learning techniques to sentiment analysis

Today, we live in a `data age'. Due to rapid increase in the amount of user-generated data on social media platforms like Twitter, several opportunities and new open doors have been prompted for organizations that endeavour hard to keep a track on customer reviews and opinions about their products. Twitter is a huge fast emergent micro-blogging social networking platform for users to express their views about politics, products sports etc. These views are useful for businesses, government and individuals. Hence, tweets can be used as a valuable source for mining public's opinion. Sentiment analysis is a process of automatically identifying whether a user-generated text expresses positive, negative or neutral opinion about an entity (i.e. product, people, topic, event etc). The objective of this paper is to give step-by-step detail about the process of sentiment analysis on twitter data using machine learning. This paper also provides details of proposed approach for sentiment analysis. This work proposes a Text analysis framework for twitter data using Apache spark and hence is more flexible, fast and scalable. Naïve Bayes and Decision trees machine learning algorithms are used for sentiment analysis in the proposed framework.