Amplifying the Polarity Categorization on Twitter Data Using Tweet Polarizer Algorithm and Emoticons Score

Abstract

Mining is utilized to help individuals to separate important data from huge amount of information. Opinion Mining or Sentiment Analysis concentrates on the exploration and grasp of the feelings from the content generated in social media. It recognizes the supposition or attitude that an individual has towards a point or an article and it looks to distinguish the perspective hidden in the large content range. Knowing clients’ sentiments and giving the best arrangement or administration is an outstanding business sector procedure pursued by each business. In this paper, we center around producing polarities of tweets to know the general feeling on a given word or a trump card string. An algorithm Tweet Polarizer is used in this paper to categorize the tweets. A couple of NLP procedures are used to develop a superior methodology for making the most appropriate and possible fringe for a given tweet and to imagine the few trademark features of customers like from which area he has posted the tweet and when.