Transforming tweets into opinions: a deep learning approach to analyse and predict election result using social media data

Social media has emerged as one major medium through which most of its users express their thoughts and opinions on any topic of debate. X has approximately 250 million daily visitors worldwide, out of which there are roughly 25 million in India. An innumerable number of views are expressed on issues like elections through tweets, using hashtags and keywords, making a large chunk of political content. Sentiment analysis on these tweets can be done in order to extract public sentiment toward the elections. This analysis can also help political parties strategize their campaigns more effectively. During elections, especially to conduct exit polls, media houses and research organizations invest huge resources in collecting public opinions. These enormous amounts of data bring the opportunity to utilize advanced models of Natural Language Processing in capturing political sentiments accurately. It is also possible to obtain more accurate results in elections by utilizing or manipulating such data for the evaluation of the voters' temporal mood. ElecSentimentNet offers an all-encompassing approach for the sentiment analysis of tweets to analyze and predict elections. Twitter embeddings with semantic enhancement have been developed with the help of a RoBERTa-based architecture, and political sentiments have been evaluated with the help of a Bi-LSTM Dense layer classifier, offering a reliable alternative to the traditional exit polls. The results obtained for the present study present the potential for the use of social media data for political analysis with useful information for political campaigns and parties.