Twitter Sentiment Analysis Using Lexical or Rule Based Approach: A Case Study

Opinion analysis or sentiment analysis is one of the most effective techniques these days in order to determine the sentiments or emotions of people regarding any event. This technique has come to the fore because of extensive use of social media platforms like Facebook, Twitter etc. by people to express their emotions regarding any event that has occurred or any event that is most likely to happen, be that the release of a movie or a political rally that is about to take place. People make sure to express their sentiments. Sentiment analysis proves very beneficial for any company selling a product to know how their product was received by people or by any political party to determine how people are reacting towards their running candidate. In order to analyze these sentiments two approaches of machine learning can be used - unsupervised or supervised. In this paper we have worked with Unsupervised Approach which is a rule based or lexical approach and can be done using the pre-built open source libraries like TextBlob, VADER.