An innovative and efficient method for Twitter sentiment analysis

Abstract

The research in sentiment analysis is one of the most accomplished fields in data mining area. Specifically, sentiment analysis centres on analysing attitudes and opinions relating a particular topic of interest using machine learning approaches, lexicon-based approaches or hybrid approaches. Users are purposive to develop an automated system that could identify and classify sentiments in the related text. An efficient approach for predicting sentiments would allow us to bring out opinions from the web contents and to predict online public choices, which could prove valuable for ameliorating changes in the sentiment of Twitter users. This paper presents a proposed model to analyse the brand impact using the real data gathered from the micro blog, Twitter collected over a period of 14 months and also discusses the review covering the existing methods and approaches in sentiment analysis. Twitter-based information gathering techniques enable collecting direct responses from the target audience; it provides valuable understanding into public sentiments in the prediction of an opinion of a particular product. The experimental result shows that the proposed method for Twitter sentiment analysis is the best, with an unrivalled accuracy of 86.8%.