Analyzing Public Sentiment on Electric Vehicles Through BERTopic and Emotion-Based Data Clustering
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".