Twitter sentiment analysis of game reviews using machine learning techniques

Sentiment analysis is basically analysing of the sentiments from the text. Sentiment analysis can be referred as opinion mining. Sentiment analysis finds and justifies the sentiment of the person with respect to a given source of content. The growth in micro-blogging activity on sites over the last few years has been increasing. Microblogging sites like twitter contain a large amount of data, which helps the companies to know what public is thinking of them. Sentiment analysis of this large data is very useful to express the opinion of the group of people. Twitter sentiment analysis is tricky as compared to broad sentiment analysis because of the slang words and misspellings and repeated characters. So it is very important to identify exact sentiment of each word. In our paper to obtain a highly accurate model of sentiment analysis of tweets with respect to latest reviews of Games which include both mobile and PC. With the help of feature selection and classifiers such as Support vector machine (SVM), Naïve Bayes and Maximum Entropy. By adding feature selection to the classifier we can select the relevant features. We will classify these tweets as positive, negative and neutral to give sentiment of each tweet and compare their results based on appropriate models.