Electric vehicle policies and public response: A public opinion analysis based on Twitter (X) data
2026
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Advisor: Prof. Dr. Handan ÇAM
Abstract (EN)
This thesis, titled “Electric Vehicle Policies and Public Reaction: A Public Opinion Analysis Based on Twitter (X) Data,” aims to examine the public’s perceptions, attitudes, and reactions toward Battery Electric Vehicles (BEVs) through social media interactions. With the widespread adoption of Electric Vehicle (EV) technologies, significant changes are taking place in the areas of environmental sustainability, energy policies, and individual consumption behaviors. In this context, the analysis of social media data offers an effective method for understanding public opinion regarding this transformation. In this study, a total of 30,363 tweets were collected from the social media platform Twitter (X) using the Python programming language, the Pandas library, and the Selenium tool. Following data cleaning and preprocessing stages, a final dataset consisting of 28,596 tweets was obtained and used in the analyses. Subsequently, the BERT Sentiment Analysis model for Turkish was applied to classify the posts as positive, negative, or neutral. The analysis revealed that user discourse primarily centered on economic costs, charging infrastructure, domestic production, technological developments, and user experience. It was also determined that topics such as environmental benefits and energy savings featured prominently in user evaluations. The findings indicate that both interest in innovative technologies and economic and infrastructure-related concerns play a decisive role in the public's attitude toward Battery Electric Vehicles (BEVs). As one of the pioneering studies examining public opinion on electric vehicle policies in Turkey using social media data, this research aims to provide a new perspective on sustainable transportation policies.
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Bahtiyar Yilmaz (Master Thesis). Electric vehicle policies and public response: A public opinion analysis based on Twitter (X) data, 2026, pp. 1-1, Gümüşhane University, Yönetim Bilişim Sistemleri Bölümü, DOI: https://doi.org/10.71008/gumushane.thesis.2026.228.
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