Sentiment Analysis is a way of considering and grouping of opinions or views expressed in a text. In this age when social media technologies are generating vast amounts of data in the form of tweets, Facebook comments, blog posts, and Instagram comments, sentiment analysis of these user-generated data provides very useful feedback. Since it is undisputable facts that twitter sentiment analysis has become an effective way in determining public sentiment about a certain topic product or issue. Thus, a lot of research have been ongoing in recent years to build efficient models for sentiment classification accuracy and precision. In this work, we analyse twitter data using support vector machine algorithm to classify tweets into positive, negative and neutral sentiments. This research try to find the relationship between feature hash bit size and the accuracy and precision of the model that is generated. We measure the effect of varying the feature has bit size on the accuracy and precision of the model. The research showed that as the feature hash bit size increases at a certain point the accuracy and precision value started decreasing with increase in the feature hash bit size.

Sentiment Analysis; Machine Learning; Support Vector Machine; Feature Hashing