Human behavior analysis on political retweets using machine learning algorithms

Abstract

The exponential rise in the use of social media has resulted in a massive increase in the volume of unstructured text created. This content is presented through messages, conversations, postings, and blogs. Microblogging has become a popular way for people to share what they are thinking. Many people express their thoughts on various issues relating to their hobbies. As a result, microblogging websites have become a valuable resource for opinion mining and sentiment research. Twitter is a well-known microblogging network, with over 500 million new tweets posted daily. The goal of this study was to mine tweets for political sentiments. The extraction of tweets relating to India's well-known political leaders of different states & parties in India and applying the polarity detection analysis of human behavior on the retweeted messages As a result, the sentiment classification algorithm is designed to determine whether tweets are more likely to predict the popularity of certain politicians among the general public. The subjectivity and polarity present in the tweets of political leaders are compared. The engagements of these leaders are then taken into account to determine their popularity. All these comparisons are then portrayed using data visualizations.

Keywords

• Political retweet
• Opinion mining
• Text classification
• Twitter
• Computing methodologies
• Machine learning algorithms