Sentiment Analysis of Imran Khan’s Tweets
Keywords:Sentiment analysis, tweets, opinion, polarity, Imran Khan
In the recent past, sentiment analysis has been an area of interests of psychologists, sociologists, neurologists, computer scientists, and linguists including corpus linguists and computational linguists. Interdisciplinary approaches to researching various issues especially the analysis of social media websites such as Facebook, Twitter, and Instagram are becoming popular nowadays. The availability of data on social media has made it easier to analyse the opinion or sentiments of its users. Analysis of these sentiments could reveal the face of users and it could help in various decision-making processes. Sentiment analysis is a system of knowing polarity (positive, negative, and neutral) in discourse. Moreover, sentiments can enable and disable certain functions of discourse and can divert the attention of the audience from important to a less important issue or otherwise, hence, there is a need to analyse the sentiments. In this research, sentiments (Polarity) of Imran Khan’s tweets are analysed with the help of R studio. Data for this study is collected from Imran Khan’s one-year’s tweets, tweeted from 1st January 2018 to 20th November 2018. Later we saved the data in. csv files. The results of the polarity check revealed that he has used all three types of sentiments that is positive, negative, and neutral. However, he mostly used neutral or free polarity items (FPIs) that is 67.41% in his tweets. Among positive and negative polarity items the number of negative polarity items (NPIs) is higher that is 23.21% as compared to positive polarity items (PPIs) which are only 9.40%. The manual analysis of results revealed that only software is not enough and there is a need to check the accuracy of the results manually. The use of negative polarity/negative face reveals that he tries to be independent and autonomous in his decisions (Goffman, 1967). The use of positive polarity items shows he tries to show his positive face to others. Moreover, sentiment analysis demonstrates the presence of themes propagated through the use of various lexical items.
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