.png)
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.
Keywords
Full Text:
PDFReferences
X. Guo, X. Zhang, and X. Zhang, “Incentive-oriented power‑carbon emissions trading-tradable Green Certificate integrated market mechanisms using multi-agent deep reinforcement learning,” Applied Energy, vol. 357, no. 1, pp. 1–12, 2024. doi:10.1016/j.apenergy.2023.122458
Y. Zhang, S. Zeng, Q. Wu, J. Fu, and T. Li, “A study on the impact of the carbon emissions trading policy on the mining industry based on Porter hypothesis,” Resources Policy, vol. 87, no. b, pp. 1–11, 2023. doi:10.1016/j.resourpol.2023.104349
A. Kavasseri Venkitaraman and V. Satya Rahul Kosuru, “Trends and challenges in Electric Vehicle Motor Drivelines - A Review,” International journal of electrical and computer engineering systems, vol. 14, no. 4, pp. 485–495, 2023. doi:10.32985/ijeces.14.4.12
P. M. Schwarz, “Electric vehicles,” Energy Economics, vol. 2, no. 1, pp. 322–346, 2022. doi:10.4324/9781003163190-18
D. Paulsen, Y. Govind, and A. Doan, “Sparkly: A simple yet surprisingly strong TF/IDF blocker for entity matching,” Proceedings of the VLDB Endowment, vol. 16, no. 6, pp. 1507–1519, 2023. doi:10.14778/3583140.3583163
B. Kabra and C. Nagar, “Convolutional Neural Network based sentiment analysis with TF-IDF based vectorization”, J Integr Sci Technol, vol. 11, no. 3, p. 503, Jan. 2023.
Q. Do, M. A. Moriyani, C. Le, and T. Le, “Cost-weighted TF-IDF: A novel approach for measuring highway project similarity based on pay items’ cost composition and term frequency,” Journal of Construction Engineering and Management, vol. 149, no. 8, pp. 1–12, 2023. doi:10.1061/jcemd4.coeng-13023
M. A. Gandhi, V. Karimli Maharram, G. Raja, S. P. Sellapaandi, K. Rathor and K. Singh, "A Novel Method for Exploring the Store Sales Forecasting using Fuzzy Pruning LS-SVM Approach," 2023 2nd International Conference on Edge Computing and Applications (ICECAA), Namakkal, India, 2023, pp. 537-543, doi: 10.1109/ICECAA58104.2023.10212292.
P. Manoharan et al., “SVM‐based Generative Adverserial Networks for Federated Learning and edge computing attack model and outpoising,” Expert Systems, vol. 40, no. 5, pp. 1–13, 2022. doi:10.1111/exsy.13072
G. Simpkins, “Benefits of electric vehicle adoption,” Nature Reviews Earth and Environment, vol. 4, no. 7, pp. 432–432, 2023. doi:10.1038/s43017-023-00465-2
N. Virmani et al., “Mitigating barriers to adopting electric vehicles in an emerging economy context,” Journal of Cleaner Production, vol. 414, no. 1, pp. 1–13, 2023. doi:10.1016/j.jclepro.2023.137557
X. Shen et al., “The impact of co-adopting electric vehicles, solar photovoltaics, and battery storage on electricity consumption patterns: Empirical evidence from Arizona,” Resources, Conservation and Recycling, vol. 192, no. 1, pp. 106914–106927, 2023. doi:10.1016/j.resconrec.2023.106914
J. Park, K. Han, H.-S. Choi, and I. S. Park, “Cooling and dynamic performance of electric vehicle traction motor adopting direct slot cooling method,” International Communications in Heat and Mass Transfer, vol. 147, no. 1, pp. 106970–106982, 2023. doi:10.1016/j.icheatmasstransfer.2023.106970
[2] W. Chen et al., “Health-considered energy management strategy for fuel cell hybrid electric vehicle based on improved soft actor critic algorithm adopted with beta policy,” Energy Conversion and Management, vol. 292, no. 1, pp. 117362–117375, 2023. doi:10.1016/j.enconman.2023.117362
F. Ouren, D. Trinko, T. Coburn, S. Simske, and T. H. Bradley, “Developing a profile of medium- and heavy-duty electric vehicle fleet adopters with text mining and machine learning,” Renewable Energy Focus, vol. 46, no. 1, pp. 303–312, 2023. doi:10.1016/j.ref.2023.07.004
S. Pérez-González, “Evidence of mechanisms in evidence-based policy,” Studies in History and Philosophy of Science, vol. 103, no. 1, pp. 95–104, 2024. doi:10.1016/j.shpsa.2023.11.006
F. Khasanah and S. Sarmini, “Implementation of Scrum Method for Keep Wallet Application Design,” Int. J. Informatics Inf. Syst., vol. 6, no. 3, pp. 103–113, Sep. 2023
Yuhastihar, Hamidah, and Madhakomala, “Evaluation of the National Values Construction Program for National Resilience Institute Republic of Indonesia (LEMHANNAS RI)”, Int. J. Appl. Inf. Manag., vol. 2, no. 4, pp. 58–72, May 2022.
R. Chandra, K. Chaudhary, and A. Kumar, “Comparison of Data Normalization for Wine Classification Using K-NN Algorithm,” Int. J. Informatics Inf. Syst., vol. 5, no. 4, pp. 175–180, Dec. 2022
D. G. Dong, “Design and Development of Intelligent Logistics Tracking System Based on Computer Algorithm”, Int. J. Appl. Inf. Manag., vol. 3, no. 2, pp. 58–69, Jul. 2023.
L. Gomes, R. da Silva Torres, and M. L. Côrtes, “Bert- and TF-IDF-based feature extraction for long-lived bug prediction in Floss: A Comparative Study,” Information and Software Technology, vol. 160, no. 1, pp. 107217–107230, 2023. doi:10.1016/j.infsof.2023.107217
H. Yazdian, N. Salmani-Dehaghi, and M. Alijanian, “A spatially promoted SVM model for Grace Downscaling: Using ground and satellite-based datasets,” Journal of Hydrology, vol. 626, no. 1, pp. 130214–130227, 2023. doi:10.1016/j.jhydrol.2023.130214
F. Z. Latrech, A. B. Rhouma, and A. Khedher, “FPGA implementation of a robust DTC-SVM based Sliding Mode Flux Observer for a double star induction motor: Hardware in the loop validation,” Microelectronics Reliability, vol. 150, no. 1, pp. 115118–115132, 2023. doi:10.1016/j.microrel.2023.115118
Z. Lian, Y. Ma, M. Li, W. Lu, and W. Zhou, “Discovery precision: An effective metric for evaluating performance of machine learning model for Explorative Materials Discovery,” Computational Materials Science, vol. 233, no. 1, pp. 112738–112746, 2024. doi:10.1016/j.commatsci.2023.112738
DOI: https://doi.org/10.47738/jads.v5i1.162
Refbacks
- There are currently no refbacks.
Journal of Applied Data Sciences
| ISSN | : | 2723-6471 (Online) |
| Publisher | : | Bright Publisher |
| Website | : | http://bright-journal.org/JADS |
| : | taqwa@amikompurwokerto.ac.id (principal contact) | |
| support@bright-journal.org (technical issues) |
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0