Assessing the Role of Positive and Negative Tweets in Predicting Electoral Outcomes: A Study of the 2019 Lok Sabha and 2020 U.S. Elections

This research paper aims to ascertain whether positive or negative tweets are a more effective indicator for predicting electoral outcomes. The investigation primarily focuses on two major elections: the 2019 Indian Lok Sabha election and the 2020 U.S. presidential election. The study compares sentiment trends of Twitter users against real-world election results. Tweets about the main political parties in each election were collected from Kaggle, a public data source. Each tweet was analyzed using the RoBERTa sentiment analysis model, which assigned positive, negative, and neutral sentiment scores. The strongest positive or negative score was recorded as the final sentiment for each tweet. After classifying the tweets, the ratio of positive tweets between the two parties was calculated and compared to the ratio of votes won in the actual election. Similarly, the ratio of negative tweets between the two parties was calculated and compared to the ratio of votes lost. The findings show that while both forms of sentiment are relevant, positive tweets align more closely with electoral success. In contrast, negative tweets do not reliably predict losses. This suggests that supportive sentiment expressed online may be a stronger indicator of real-world outcomes than negative sentiment alone.