Sentiment Analysis of Reviews using Machine Learning

Sentiment Analysis (SA) is breaking new grounds in the field of data analysis, it has gained the limelight of many researchers, forasmuch as analysis of twitter text is worthy and favorable to the company as well as customers in many different aspects. This paper gives us a glimpse about how tweets can be analyzed and utilized by the organizations with the ability to scrutinize communities’ emotion towards the events or products interrelated to them. Sorting out thousands of tweets would be an arduous task for a human to yield a potential result regarding that particular topic. Our objective is based on the approach of classifying tweets into three categories which can be positive negative or neutral. We made certain to use a classification strategy based on Naive Bayes (NB) because it is a facile and intuitive method for analysis, NB combine efficiency with plausible accuracy. The result of the sentiment analysis on twitter data will be displayed in a graph with different sections presenting positive, negative and neutral sentiments. This helped us to bring to a successful conclusion in defining particular tweets and built a review of a particular product.