Analyzing Public Sentiment on Electric Vehicles Through BERTopic and Emotion-Based Data Clustering - Kamal H. Jihad; Azhar Ahmed Bilal; Mohammed Rashad Baker; Yaser Issam Aljanabi (Ikatan Ahli Informatika Indonesia (IAII), 2025)
The escalating impact of technological advancements on worldwide society prompts a closer examination of their profound consequences. Enhanced communication methods and the significant influence of social media platforms stand out as critical factors, with the automotive industry responding to environmental concerns through the emergence of electric vehicles (EVs). In this work the relationship between the trends of EV evolving and social media was utilized using X (aka, Twitter) data. Specifically, this work studies the increasing market demand for EVs due to the impact of social media. Consequently, the study is crucial for both clients and EV manufacturers. To identify the primary discussion themes on Twitter, this article utilizes a topic modelling technique (BERTopic) a data mining method and analyses the production and sales of EV manufacturers. We utilized The National Research Council Canada's Emotion Lexicon (NRCLex) for emotion analysis. Trust, surprise, anger, anticipation, positive, negative, disgust, fear, sadness, and joy are the eight emotions of NRCLex that can provide awareness of the present dynamics. We compared current media coverage of EVs and topic-modeled data. The results showed that BERTopic and NRCLex provided a depth of analysis via the emotional analysis. Consequently, this study contributes to improving the understanding of public sentiment's influence on EV trends.