Sentiment Base Emotions Classification of Celebrity Tweets by Using R Language

Twitter is considered as one of the most effective microblogging site(s) developed mainly to express the views and thoughts of its users. Twitter users follow their favourite personalities, i.e., celebrities, and use to tweet/retweet, frequently, without knowing the emotions behind the made tweet. This research focuses on learning the emotions behind the tweets using R language by formulating it as an emotion classification problem. We applied twitter scraper data scraping technique to collect the dataset from the twitter accounts for our analysis. By using the proposed scheme, we estimate the emotions (whether the person was happy, sad, anxious, joyous, angry, surprised or feared) behind the tweet. We believe that our scheme would help users understand more the personality insights from the tweets. Sentiment R Package of R programming language is used to find out the personality insight than the positive, negative and neutral emoticon combined to find out the accurate results. If the user has more negative tweets we can say that he is happy and joys or if the negative tweets are more than positive tweets we can quickly evaluate that the personality is sad, angry, anxious or feared. The main contribution of this paper is to identify the emotions and the trend of the characters or twitter users.