Feasible Trend Prediction for 2019 Indian General Elections

Abstract

The popularity and accessibility of social media applications, such as Twitter, has increased over the past few years at a rapid pace. Users from different parts of the world use it to share thoughts with each other. Politicians use these platforms at every viable opportunity and increase support and following for themselves and their parties. Sentiment analysis has become a key methodology to gain insight from social networks. We perform sentiment analysis using a lexicon-based sentiment analyser and sentiment trend prediction for a short interval of time to model the sentiment trend towards the top contenders of the Indian General Election 2019 on twitter. Sentiment Analysis is done on the tweets posted during the campaign period of the Indian General Elections of 2019. This is done using a pre-trained sentiment analyser called VADER of the NLTK library. VADER is optimized for social media data and can produce useful results. The dataset used for this paper is created using web scraping modules written in Python. In addition to that, sentiment trend prediction was done for a period of 10 days from the day of result announcement using Linear Regression, to achieve sustainable reports.