On the frontiers of Twitter data and sentiment analysis in election prediction: a review

Abstract

Election prediction using sentiment analysis is a rapidly growing field that utilizes natural language processing and machine learning techniques to predict the outcome of political elections by analyzing the sentiment of online conversations and news articles. Sentiment analysis, or opinion mining, involves using text analysis to identify and extract subjective information from text data sources. In the context of election prediction, sentiment analysis can be used to gauge public opinion and predict the likely winner of an election. Significant progress has been made in election prediction in the last two decades. Yet, it becomes easier to have its comprehensive view if it has been appropriately classified approach-wise, citation-wise, and technology-wise. The main objective of this article is to examine and consolidate the progress made in research about election prediction using Twitter data. The aim is to provide a comprehensive overview of the current state-of-the-art practices in this field while identifying potential avenues for further research and exploration.

Synthesis of Slogans with Predicted Sentiment from Twitter using a Novel Hybrid SDG-LSTM Model for Election Campaigns

“…These generated slogans embody a spectrum of emotions and ideas prevalent in society. From fostering a sense of unity and kindling optimism to championing the values of resilience and empowerment, each slogan is meticulously crafted to mirror the evolving emotional landscape of our culture (9) . They serve as more than mere words; they reflect a collective voice, a dynamic representation of the thoughts, hopes, and aspirations of Twitter users.…”