Computational Modelling of World Leaders’ Covid -19 Opinions: A Sentiment Analysis Approach

Sentiment analysis uses the processing of Natural language Processing (NLP) together with analysis of text and linguistic patterns to help in identifying the state of information which has been mined from reliable sources. The polarization of a textual pattern, whether a linguistic pattern is positive, neutral or negative can be evaluated with the help of computational modelling of the pattern. Performing computational modelling on sentiment analysis can bring out useful patterns which can be employed for monitoring purposes in social media. Sentiment analysis also finds its vast applications in the management and social aspects of sciences due to its varied importance to commercial applications and society. The growing prominence of sentiment analysis matches with the exponential progress of social media such as Facebook, Twitter, and other social networks. This research paper recommends a standalone platform which allows data handlers to execute sentiment analysis which employs Twitter API to collect information about COVID- 19 related tweets of world leaders. The proposed model is used to analyse whether the sentiment in a tweet is positive, neutral or negative. The pre-processed data passes through the sentiment generation phase where sentiment classes are demarcated for polarity. In this research paper, tweets of various world leaders are labelled with sentiments, and the overall sentiment of their tweets are analysed.