A natural language processing framework for analyzing public transportation user satisfaction: a case study

Abstract

Public transportation services make an important contribution to the nation's economy. However, the public transportation system was significantly impacted both during and after the Covid-19 outbreak. To minimize these impacts, it is important to know the users' sentiment and improve the service quality accordingly to change the users' attitude towards public transportation systems. Natural language processing is used to make meaningful inferences about user sentiment using various analysis techniques. Historically, surveys have also been used for years to learn users' opinions about transportation services. In this study, this traditional method was used to determine the satisfaction of public transportation users. The categorization model employed in the system developed as part of this work is based on algorithms such as Long Short-Term Memory (LSTM), Random Forest (RF), and Multi Logistic Regression (MLR). The dataset contains information gathered from the online survey. Of the models created utilizing the training dataset, it was discovered that the LSTM model offered the highest accuracy. Users' comments can help improve public transportation operators' operations, improve service quality, and monitor actions accordingly. Therefore, in this study, users' emotions were classified as positive, negative, or neutral based on the comments.

Keywords

• Public transportation
• service quality
• natural language processing
• sentiment analysis
• machine learning

References

1. Union Internationale des Transports Publics. (2022). UITP Worldwide Europe. Last Accessed December 20, 2022. https://www.uitp.org/regions/europe/
2. Kanda, W., & Kivimaa, P. (2020). What opportunities could the COVID-19 outbreak offer for sustainability transitions research on electricity and mobility?. Energy Research & Social Science, 68, 101666. https://doi.org/10.1016/j.erss.2020.101666
3. El-Diraby, T., Shalaby, A., & Hosseini, M. (2019). Linking social, semantic and sentiment analyses to support modeling transit customers’ satisfaction: Towards formal study of opinion dynamics. Sustainable Cities and Society, 49, 101578. https://doi.org/10.1016/j.scs.2019.101578
4. Liu, Y., Li, Y., & Li, W. (2019). Natural language processing approach for appraisal of passenger satisfaction and service quality of public transportation. IET Intelligent Transport Systems, 13(11), 1701-1707. https://doi.org/10.1049/iet-its.2019.0054
5. Öğe, B. C., & Kayaalp F., (2021). Farklı Sınıflandırma Algoritmaları ve Metin Temsil Yöntemlerinin Duygu Analizinde Performans Karşılaştırılması. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 9(6), 406-416. https://doi.org/10.29130/dubited.1015320
6. Collins, C., Hasan, S., & Ukkusuri, S. V. (2013). A novel transit rider satisfaction metric: Rider sentiments measured from online social media data. Journal of Public Transportation, 16(2), 21-45. https://doi.org/10.5038/2375-0901.16.2.2
7. Effendy, V., Novantirani, A., & Sabariah, M. K. (2016). Sentiment analysis on Twitter about the use of city public transportation using support vector machine method. Intl. J. ICT, 2(1), 57-66. https://doi.org/10.21108/IJOICT.2016.21.85
8. Taskin, S. G., Kucuksille, E. U., & Topal, K. (2022). Detection of Turkish fake news in Twitter with machine learning algorithms. Arabian Journal for Science and Engineering, 47(2), 2359-2379. https://doi.org/10.1007/s13369-021-06223-0

9. Schweitzer, L. (2014). Planning and social media: a case study of public transit and stigma on Twitter. Journal of the American Planning Association, 80(3), 218-238. https://doi.org/10.1080/01944363.2014.980439
10. Luong, T. T., & Houston, D. (2015). Public opinions of light rail service in Los Angeles, an analysis using Twitter data. IConference 2015 Proceedings.
11. Nik Bakht, M., Kinawy, S. N., & El-Diraby, T. E. (2015). News and social media as performance indicators for public involvement in transportation planning: Eglinton Crosstown Project in Toronto, Canada (No. 15-0117).
12. Lock, O., & Pettit, C. (2020). Social media as passive geo-participation in transportation planning–how effective are topic modeling & sentiment analysis in comparison with citizen surveys?. Geo-spatial Information Science, 23(4), 275-292. https://doi.org/10.1080/10095020.2020.1815596
13. Liu, X., Ye, Q., Li, Y., Fan, J., & Tao, Y. (2021). Examining public concerns and attitudes toward unfair events involving elderly travelers during the COVID-19 pandemic using Weibo data. International Journal of Environmental Research and Public Health, 18(4), 1756. https://doi.org/10.3390/ijerph18041756
14. Vasquez-Henriquez, P., Graells-Garrido, E., & Caro, D. (2019, June). Characterizing transport perception using social media: differences in mode and gender. In Proceedings of the 10th ACM Conference on Web Science (pp. 295-299). https://doi.org/10.1145/3292522.3326036
15. Das, R. D., & Purves, R. S. (2019). Exploring the potential of Twitter to understand traffic events and their locations in Greater Mumbai, India. IEEE Transactions on Intelligent Transportation Systems, 21(12), 5213-5222. https://doi.org/10.1109/TITS.2019.2950782
16. Sala, L., Wright, S., Cottrill, C., & Flores-Sola, E. (2021). Generating demand responsive bus routes from social network data analysis. Transportation Research Part C: Emerging Technologies, 128, 103194. https://doi.org/10.1016/j.trc.2021.103194
17. Nurthohari, Z., Sensuse, D. I., & Lusa, S. (2022, July). Sentiment Analysis of Jakarta Bus Rapid Transportation Services using Support Vector Machine. In 2022 International Conference on Data Science and Its Applications (ICoDSA) (pp. 171-176). IEEE. https://doi.org/10.1109/ICoDSA55874.2022.9862903
18. Wang, S., Li, M., Yu, B., Bao, S., & Chen, Y. (2022). Investigating the Impacting Factors on the Public’s Attitudes towards Autonomous Vehicles Using Sentiment Analysis from Social Media Data. Sustainability, 14(19), 12186. https://doi.org/10.3390/su141912186
19. Şahin, G. (2017). Turkish document classification based on Word2Vec and SVM classifier. In 2017 25th signal processing and communications applications conference (SIU) (pp. 1-4). IEEE. https://doi.org/10.1109/SIU.2017.7960552
20. Kemaloğlu N., Küçüksille E., and Özgünsür M. E. (2021). Turkish sentiment analysis on social media. Sakarya University Journal of Science, 25(3), 629-638. https://doi.org/10.16984/saufenbilder.872227