Topic Modeling as a Tool to Gauge Political Sentiments from Twitter Feeds

Abstract

As ubiquitous as it is, the Internet has spawned a slew of products that have forever changed the way one thinks of society and politics. This article proposes a model to predict chances of a political party winning based on data collected from Twitter microblogging website, because it is the most popular microblogging platform in the world. Using unsupervised topic modeling and the NRC Emotion Lexicon, the authors demonstrate how it is possible to predict results by analyzing eight types of emotions expressed by users on Twitter. To prove the results based on empirical analysis, the authors examine the Twitter messages posted during 14th Gujarat Legislative Assembly election, 2017. Implementing two unsupervised clustering methods of K-means and Latent Dirichlet Allocation, this research shows how the proposed model is able to examine and summarize observations based on underlying semantic structures of messages posted on Twitter. These two well-known unsupervised clustering methods provide a firm base for the proposed model to enable streamlining of decision-making processes objectively.