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

Introduction

The emergence of micro blogging sites has ushered in an immense wave of outpouring of public sentiment, views, and opinions. Peoples across the globe can, with a few keystrokes, make their views known to others of diverse social, religious, cultural and geographic backgrounds. No longer is the word of mouth or ideas confined to a localized section of society. The power and reach of the almost all-encompassing worldwide web have now made it possible for millions to view a microblog in seconds and at almost a press of a button irrespective of geographical distances involved.

Analysis of tweets related to politics can reveal trends. Evaluation of trends is imperative for ascertaining breaking news, generate friend recommendations, conduct data mining on opinions espoused by users, and record sentiments (Alvarez-Melis, 2016). Natural Language Processing (NLP) allows to classify opinions expressed through posts on Twitter and Facebook. NLP is comprised of opinion mining and text mining (Zhang & Liu, 2017). The impact of mining data from microblogging sites is significant. Insofar as analyzing the views on new musical releases, new product launches, motion picture releases, and similar other entertainment features, mining of tweets has been shown to be instrumental in helping formulate strategies accordingly (Mohey & Mohamed, 2016).