Sentiment analysis of Twitter data: Case study on digital India

Nowadays Opinion Mining has become an emerging topic of research due to lot of opinionated data available on Blogs & social networking sites. Tracking different types of opinions & summarizing them can provide valuable insight to different types of opinions to users who use Social networking sites to get reviews about any product, service or any topic. Analysis of opinions & its classification on the basis of polarity (positive, negative, neutral) is a challenging task. Lot of work has been done on sentiment analysis of Twitter data and lot needs to be done. In our work we are trying to perform sentiment analysis of the Twitter data set that expresses opinion about Modi ji's Digital India Campaign. In my work, I have collected these sentiments and classified polarity of sentiments in these opinions w.r.t. Positive, Negative or Neutral. Twitter data is collected for analysis using Twitter API. Out of the two widely used approaches used for sentiment analysis, Machine Learning & Dictionary Based approach, we are using Dictionary Based approach to analyze data posted by different users. Then polarity classification of this data is done. In this paper we discuss sentiment analysis of Twitter data, existing tools available for sentiment analysis, related work, framework used, case study to demonstrate the work followed by the results section. Results clearly demonstrate that the 50% of the collected opinions are positive, 20% are Negative and rests 30% are neutral.