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, with approximately 57.3% of comments expressing negative sentiment, 33.2% positive, and the rest neutral. This study makes a unique contribution to the existing literature by shedding light on the nuanced perspectives of the public, revealing strong support for incentives from a financial standpoint, coupled with notable concerns about electric vehicle prices and charging infrastructure availability. External factors such as government policies and vehicle prices significantly influence public sentiment, and easy access to charging infrastructure plays a crucial role in shaping positive sentiment. Furthermore, the study emphasizes the positive influence of environmental concerns on public support for electric vehicle incentives. The results provide valuable insights into public sentiment, contributing to a better understanding of the factors influencing it, and offer practical policy recommendations for the design and implementation of effective electric vehicle incentives to foster sustainable and environmentally friendly transportation.