Model machine learning for sentiment analysis of the presence of electric vehicle in Indonesia
Keywords:
Machine learning, Sentiment analysis, Electric vehicleAbstract
Sentiment analysis, also known as opinion analysis or social sentiment analysis, is a well-established field of study. Within the automotive industry, great attention is being paid to the presence of electric cars as a viable solution to the pressing issue of greenhouse gas emissions. In order to gauge the level of acceptance and adoption of this technology, it is crucial to analyze the sentiments and opinions expressed by individuals towards electric cars Various approaches can be employed for sentiment analysis, including rule-based techniques, statistical methods, and machine learning algorithms. The objective of this research endeavor is to conduct sentiment analysis on online publications and social media discussions pertaining to electric cars. Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF) are the specific methods employed in this study. The effectiveness of these methods is evaluated using accuracy measurements and Receiver Operating Characteristic (ROC) analysis. The accuracy outcomes attained by LR were 78.02%, SVM achieved 71.92%, and RF exhibited 82.35%. By virtue of the examination outcomes of multiple techniques utilized, there is an optimistic expectation that this can serve as the initial stride towards constructing sentiment applications for the existence of electric cars in the Indonesian context.
References
[1] K. Kolbe, “Mitigating urban heat island effect and carbon dioxide emissions through different mobility concepts: Comparison of conventional vehicles with electric vehicles, hydrogen vehicles and public transportation,” Transp. Policy, vol. 80, pp. 1–11, Aug. 2019, doi: 10.1016/j.tranpol.2019.05.007.
[2] X. Hu, R. Zhou, S. Wang, L. Gao, and Z. Zhu, “Consumers’ value perception and intention to purchase electric vehicles: A benefit-risk analysis,” Res. Transp. Bus. Manag., vol. 49, p. 101004, Aug. 2023, doi: 10.1016/j.rtbm.2023.101004.
[3] M. I. Alhari, O. N. Pratiwi, and M. Lubis, “Sentiment Analysis of The Public Perspective Electric Cars in Indonesia Using Support Vector Machine Algorithm,” in 2022 International Conference of Science and Information Technology in Smart Administration (ICSINTESA), Nov. 2022, pp. 155–160, doi: 10.1109/ICSINTESA56431.2022.10041604.
[4] N. Ashari, M. Z. Mifta Al Firdaus, I. Budi, A. B. Santoso, and P. Kresna Putra, “Analyzing Public Opinion on Electrical Vehicles in Indonesia Using Sentiment Analysis and Topic Modeling,” in 2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE), Feb. 2023, pp. 461–465, doi: 10.1109/ICCoSITE57641.2023.10127834.
[5] M. Wang, H. You, H. Ma, X. Sun, and Z. Wang, “Sentiment Analysis of Online New Energy Vehicle Reviews,” Appl. Sci., vol. 13, no. 14, p. 8176, Jul. 2023, doi: 10.3390/app13148176.
[6] M. Lu, X. Zhang, J. Ji, X. Xu, and Y. Zhang, “Research progress on power battery cooling technology for electric vehicles,” J. Energy Storage, vol. 27, p. 101155, Feb. 2020, doi: 10.1016/j.est.2019.101155.
[7] T. Capuder, D. Miloš Sprčić, D. Zoričić, and H. Pandžić, “Review of challenges and assessment of electric vehicles integration policy goals: Integrated risk analysis approach,” Int. J. Electr. Power Energy Syst., vol. 119, p. 105894, Jul. 2020, doi: 10.1016/j.ijepes.2020.105894.
[8] R. Jena, “An empirical case study on Indian consumers’ sentiment towards electric vehicles: A big data analytics approach,” Ind. Mark. Manag., vol. 90, pp. 605–616, Oct. 2020, doi: 10.1016/j.indmarman.2019.12.012.
[9] L. M. Austmann and S. A. Vigne, “Does environmental awareness fuel the electric vehicle market? A Twitter keyword analysis,” Energy Econ., vol. 101, p. 105337, Sep. 2021, doi: 10.1016/j.eneco.2021.105337.
[10] S.-C. Ma, Y. Fan, J.-F. Guo, J.-H. Xu, and J. Zhu, “Analysing online behaviour to determine Chinese consumers’ preferences for electric vehicles,” J. Clean. Prod., vol. 229, pp. 244–255, Aug. 2019, doi: 10.1016/j.jclepro.2019.04.374.
[11] Q. Qin, Z. Zhou, J. Zhou, Z. Huang, X. Zeng, and B. Fan, “Sentiment and attention of the Chinese public toward electric vehicles: A big data analytics approach,” Eng. Appl. Artif. Intell., vol. 127, p. 107216, Jan. 2024, doi: 10.1016/j.engappai.2023.107216.
[12] S. Hardman, R. Berliner, and G. Tal, “Who will be the early adopters of automated vehicles? Insights from a survey of electric vehicle owners in the United States,” Transp. Res. Part D Transp. Environ., vol. 71, pp. 248–264, Jun. 2019, doi: 10.1016/j.trd.2018.12.001.
[13] K. Chen, Z. Zhang, J. Long, and H. Zhang, “Turning from TF-IDF to TF-IGM for term weighting in text classification,” Expert Syst. Appl., vol. 66, pp. 245– 260, Dec. 2016, doi: 10.1016/j.eswa.2016.09.009.
[14] T. H. Jaya Hidayat, Y. Ruldeviyani, A. R. Aditama, G. R. Madya, A. W. Nugraha, and M. W. Adisaputra, “Sentiment analysis of twitter data related to Rinca Island development using Doc2Vec and SVM and logistic regression as classifier,” Procedia Comput. Sci., vol. 197, pp. 660–667, 2022, doi: 10.1016/j.procs.2021.12.187.
[15] W. Bourequat and H. Mourad, “Sentiment Analysis Approach for Analyzing iPhone Release using Support Vector Machine,” Int. J. Adv. Data Inf. Syst., vol. 2, no. 1, pp. 36–44, Apr. 2021, doi: 10.25008/ijadis.v2i1.1216.
[16] S. Sathyan, J. J. Peedikayil, R. P. V, and S. R. Salkuti, “Two-Layered Machine Learning Approach for Sentiment Analysis of tweets related to Electric Vehicles,” in 2023 International Conference on Innovations in Engineering and Technology (ICIET), Jul. 2023, pp. 1–6, doi: 10.1109/ICIET57285.2023.10220717.
[17] V. Breschi, M. Tanelli, C. Ravazzi, S. Strada, and F. Dabbene, “Social network analysis of electric vehicles adoption: a data-based approach,” in 2020 IEEE International Conference on Human-Machine Systems (ICHMS), Sep. 2020, pp. 1–4, doi: 10.1109/ICHMS49158.2020.9209373.
[18] H. Christian, D. Suhartono, A. Chowanda, and K. Z. Zamli, “Text based personality prediction from multiple social media data sources using pre- trained language model and model averaging,” J. Big Data, vol. 8, no. 1, p. 68, Dec. 2021, doi: 10.1186/s40537-021-00459-1.
[19] W. Zhang, T. Yoshida, and X. Tang, “A comparative study of TF*IDF, LSI and multi-words for text classification,” Expert Syst. Appl., vol. 38, no. 3, pp. 2758–2765, Mar. 2011, doi: 10.1016/j.eswa.2010.08.066.
[20] J. Cervantes, F. Garcia-Lamont, L. Rodríguez-Mazahua, and A. Lopez, “A comprehensive survey on support vector machine classification: Applications, challenges and trends,” Neurocomputing, vol. 408, pp. 189–215, Sep. 2020, doi: 10.1016/j.neucom.2019.10.118.
[21] N. Jalal, A. Mehmood, G. S. Choi, and I. Ashraf, “A novel improved random forest for text classification using feature ranking and optimal number of trees,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 6, pp. 2733–2742, Jun. 2022, doi: 10.1016/j.jksuci.2022.03.012.
[22] A. M. Siregar, Y. A. Purwanto, S. H. Wijaya, and N. Nahrowi, “Two-stages of segmentation to improve accuracy of deep learning models based on dairy cow morphology,” Int. J. Electr. Comput. Eng., vol. 13, no. 2, p. 2093, Apr. 2023, doi: 10.11591/ijece.v13i2.pp2093-2100.
[23] J. T. Wixted and L. Mickes, “Evaluating eyewitness identification procedures: ROC analysis and its misconceptions.,” J. Appl. Res. Mem. Cogn., vol. 4, no. 4, pp. 318–323, Dec. 2015, doi: 10.1016/j.jarmac.2015.08.009
