Twitter Sentiment Analysis of Current Affairs

Abstract

Sentiment Analysis is an important type of text analysis that aims to support judgment making by extracting &analyzing opinion oriented text. Identifying positive & negative opinions & measuring how positively & negatively an entity is regarded. sentiment analysis on social media data while the use of machine learning classifier for predicting the sentiment orientation provide a useful tool for users to monitor brand or product sentiment. File level sentiment analysis is used which consists of Term Frequency (TF) and Inverse Document Frequency (IDF) values as features along with Fuzzy Clustering which results in positive and negative sentiments. As more & more user articulate their views & opinion on twitter. So twitter becomes valuable sources of people?s opinions. Tweets data can be used to infer people?s outlook for marketing & social studies. Twitter sentiment analysis that can stain the general people?s opinion in regard to social event which are going to be in current on twitter. In this research will take current scenarios which are going to be on twitter as an example for sentiment analysis. In these will use the proposed feature extraction model with emoticons and Synonym using SVM classifier. Using this can obtain greater accuracy as compared to previous research work. This research is the comparative analysis with different classifiers to identify public?s opinion.