Sentiment Unleashed: Electric Vehicle Incentives Under the Lens of Support Vector Machine and TF-IDF Analysis

Abstract

This research examines public sentiment regarding electric vehicle incentives through sentiment analysis of online comments. These incentives include tax deductions and other financial rewards offered to promote the adoption of electric vehicles. In this study, the researchers collected and analyzed over 1,000 comments from various online platforms to understand the public's perspective on these incentives. The study employs Support Vector Machine (SVM), a powerful machine learning algorithm, as the main method and utilizes Term Frequency-Inverse Document Frequency (TF-IDF) to analyze comment texts. The research findings depict significant variation in public sentiment regarding electric vehicle incentives. Approximately 57.3% of comments express negative sentiment towards these incentives, while 33.2% are positive, and the rest are neutral. There is strong support for these incentives, particularly from a financial standpoint. However, some dissatisfaction is expressed, especially regarding electric vehicle prices and charging infrastructure availability. External factors such as government policies and vehicle prices significantly influence public sentiment. Easy access to charging infrastructure also plays a crucial role in shaping positive sentiment. Environmental issues also contribute to a positive view of electric vehicle incentives. Policy recommendations arising from this research emphasize the need to consider these factors when designing and implementing electric vehicle incentives. Improvement efforts in pricing, infrastructure, and environmental education can help enhance electric vehicle adoption in society. This research provides valuable insights into public sentiment towards electric vehicle incentives and the factors influencing such sentiment. The results can serve as a foundation for better decision-making to support the development of sustainable and environmentally friendly electric vehicles.