Optimizing Election Result Prediction Through Fine-Tuned Transformer Models
Sajal Singhal, Gautam Pruthi, Ayush Kumar A., Lakshay Kapoor, Vandana Bhatia · 2023
Predicting election results accurately is crucial for understanding political dynamics and making informed decisions. In recent years, Transformer-Based models have shown remarkable performance in various Natural Language Processing tasks. However, applying these models to Election Result Prediction remains a challenging task due to the complexity of political landscapes and the inherent unpredictability of voter behavior. The pervasive use of Social Media Platforms, exemplified by Twitter, has established itself as a significant channel for users to express and disseminate their sentiments and viewpoints. This widespread adoption has created novel opportunities to quantify and evaluate the popularity and public sentiment concerning political leaders. This paper introduces an innovative approach that harnesses Sentiment Analysis techniques to forecast the outcome of the 2022 Mid-Term Elections in the United States. The proposed methodology comprises of approximately 42,000 tweets and employing diverse Sentiment Analysis approaches to assess the sentiment towards each political candidate. By harnessing the copious user-generated content available on platforms like Twitter, this work uncovers valuable insights that can augment the precision of Election Result Predictions. The performance of the proposed approach is evaluated by computing sentiment scores for individual candidates and employing them to prognosticate poll results and it has been found that the proposed approach is optimal approach amongst the competent approaches for predicting election results based on sentiment analysis of social media. The findings of this paper contribute to the domain of political forecasting by demonstrating the efficacy of Sentiment Analysis in leveraging Social Media data for Election Result Prediction.