Twitter (X) as an electoral barometer: systematic evidence from sentiment analysis of Twitter data

Abstract Twitter (X) has emerged as a vital platform for understanding public opinion and forecasting election outcomes through real-time sentiment analysis. This study systematically reviews machine learning (ML) and deep learning (DL) techniques applied to Twitter data for election prediction. Following PRISMA guidelines, 275 studies were screened, and 76 met the inclusion criteria. The review identifies dominant methods, performance trends, and research gaps in this field. Results show that pre-election data are most frequently analyzed, with the United States, India, and Indonesia leading the research landscape. Term Frequency–Inverse Document Frequency (TF–IDF), often paired with n-gram models, remains the most widely adopted feature extraction technique. Naïve Bayes and Support Vector Machines consistently deliver strong predictive performance, while newer DL models such as BERT and Bi-LSTM demonstrate improved contextual understanding. Approximately 78% of reviewed studies correctly predicted real election outcomes. This review consolidates methodological evidence, highlights emerging transformer-based innovations, and outlines directions for multilingual, cross-platform, and explainable AI approaches in electoral forecasting.