Introduction: In the present scenario, the social media network plays a significant role in
sharing information between individuals. This incorporates information about news and events that are
presently occurring in the worldwide. Anticipating election results is presently turning into a fascinating
research topic through social media. In this article, we proposed a strategy to anticipate election results
by consolidating sub-event discovery and sentimental analysis in micro-blogs to break down as well as
imagine political inclinations uncovered by those social media users.
Methods: This approach discovers and investigates sentimental data from micro-blogs to anticipate the
popularity of contestants. In general, many organizations and media houses conduct a pre-poll review and
obtain expert’s perspectives to anticipate the result of the election, but for our model, we use Twitter data to
predict the result of an election by gathering information from Twitter and evaluate it to anticipate the result
of the election by analyzing the sentiment of twitter information about the contestants.
Results: The number of seats won by the first, second, and third party in AP Assembly Election
2019 has been determined by utilizing Positive Sentiment Scores (PSS’s) of the parties. The actual
results of the election and our predicted values of the proposed model are compared, and the outcomes
are very close to actual results. We utilized machine learning-based sentimental analysis to
discover user emotions in tweets, anticipate sentiment score, and then convert this sentiment score to
parties' seat score. Comprehensive experiments are conducted to check out the performance of our
model based on a Twitter dataset.
Conclusion: Our outcomes state that the proposed model can precisely forecast the election results with
accuracy (94.2 %) over the given baselines. The experimental outcomes are very closer to actual election
results and contrasted with conventional strategies utilized by various survey agencies for exit polls and
approval of results demonstrated that social media data can foresee with better exactness.
Discussion: In the future, we might want to expand this work into different areas and nations of the
reality where Twitter is picking up prevalence as a political battling tool, and where politicians and
individuals are turning towards micro-blogs for political communication and data. We would likewise
expand this research into various fields other than general elections and from politicians to