Sentiment Analysis of Traffic Accidents in Twitter Tweets Using the Naïve Bayes Method
DOI:
https://doi.org/10.65310/fn3jte47Keywords:
Sentiment Analysis, Naïve Bayes, Traffic Accident, Infrastructure Evaluation, Twitter Mining.Abstract
This study develops a robust computational framework using a structured Naïve Bayes architecture to mine, classify, and analyze public discourse regarding traffic accidents on Twitter (now X). Utilizing a massive corpus of 157,629 digital documents, the probabilistic model successfully extracts macro-level sentiment distributions with a high global accuracy of 82.60%. The empirical findings reveal an overwhelming dominance of external factor attributions, accumulating 128,613 tweets, which underscores a critical public sensitivity toward infrastructure malfunctions and suboptimal road conditions in urban environments. By transforming unorganized microblogging texts into structured analytical matrices, this research establishes an economical, real-time social sensor that effectively bypasses the logistical delays of conventional manual reporting systems. Ultimately, this study contributes a novel theoretical repositioning from reactive safety evaluations to data-driven preventive strategies, providing government authorities with a rigorous, scalable decision-making tool to optimize sustainable transportation infrastructure and mitigate urban traffic fatalities.
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Copyright (c) 2026 Aldy Sahputra Saragih, Evantrana Yordan Bangun, Fastabikha Akbar Fahlevi, Ariel Kurniawan, Kristian Sigalingging, Fasta Krel Four Wati Br Haloho, Salman Putra Jaya Hulu (Author)
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