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Original Article
Sentiment Analysis of Uber Customer Reviews Using Machine Learning and Deep Learning Techniques
Pandiri Sailaja1
Suneel Kumar Duvvuri2
1 Student, Department of Computer Science, Government College (Autonomous), Rajahmundry, Andhra Pradesh, India. 2 Assistant Professor, Department of Computer Science, Government College (Autonomous), Rajahmundry, Andhra Pradesh, India.
Published Online: March-April 2026
Pages: 378-389
Cite this article
↗ https://www.doi.org/10.59256/ijire.20260702044References
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Applications Reviews: A Comparative Analysis between Deep Learning and Machine Learning Algorithms.”
12. A. Go, R. Bhayani, and L. Huang, “Twitter Sentiment Classification using Distant Supervision.” [Online]. Available:
http://tinyurl.com/cvvg9a
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Available: http://arxiv.org/abs/1602.04874
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Apr. 2005, pp. 799–804.
16. Y. Fu and C. Soman, “Real-time Data Infrastructure at Uber,” in Proceedings of the ACM SIGMOD International Conference on
Management of Data, Association for Computing Machinery, 2021, pp. 2503–2516. doi: 10.1145/3448016.3457552.
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no. 4, pp. 308–319, 2023, doi: 10.48130/DTS-2023-0026.18. G. Zheng et al., “Learning Phase Competition for Traffic Signal Control,” May 2019, [Online]. Available:
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http://www.nber.org/papers/w22627
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22. L. Rayle, D. Dai, N. Chan, R. Cervero, and S. Shaheen, “Just a better taxi? A survey-based comparison of taxis, transit, and ridesourcing
services in San Francisco,” Transp. Policy (Oxf)., vol. 45, pp. 168–178, Jan. 2016, doi: 10.1016/j.tranpol.2015.10.004.
23. K. Lee, O. Levy, and L. Zettlemoyer, “Recurrent Additive Networks,” Jun. 2017, [Online]. Available: http://arxiv.org/abs/1705.07393
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25. G. Singh, “Sentiment Analysis of Code-Mixed Social Media Text (Hinglish).”
26. A. Alamsyah, M. Jannah, and D. Ramadhani, “Uncovering Customer Issues in E-Commerce: Sentiment Analysis and Topic Modeling
Approach,” Apr. 2023, pp. 355–360. doi: 10.1109/ICOIACT59844.2023.10455920.
27. A. Alamsyah and F. Saviera, “A Comparison of Indonesia’s E-Commerce Sentiment Analysis for Marketing Intelligence Effort (case
study of Bukalapak, Tokopedia and Elevenia).”
28. Y. Chen, Q. You, J. Yuan, and J. Luo, “Twitter sentiment analysis via bi-sense emoji embedding and attention-based LSTM,” in MM
2018 - Proceedings of the 2018 ACM Multimedia Conference, Association for Computing Machinery, Inc, Oct. 2018, pp. 117–125.
doi: 10.1145/3240508.3240533.
29. X. Hu, F. Chen, C. Ma, and W. Tan, “Comment on Security and Improvement of Partial Blind Signature Scheme and Revocable
Certificateless Signature Scheme,” in Journal of Physics: Conference Series, IOP Publishing Ltd, Mar. 2021. doi: 10.1088/1742-
6596/1827/1/012005.
30. X. Zhang and Y. LeCun, “Text Understanding from Scratch,” Apr. 2016, [Online]. Available: http://arxiv.org/abs/1502.01710
31. A. Sharma and S. Dey, “Using Self-Organizing Maps for Sentiment Analysis.”
32. L. Khan, A. Amjad, N. Ashraf, and H. T. Chang, “Multi-class sentiment analysis of urdu text using multilingual BERT,” Sci. Rep., vol.
12, no. 1, Dec. 2022, doi: 10.1038/s41598-022-09381-9
2017, [Online].Available: http://arxiv.org/abs/1706.07206
2. R. Tejwani, “Sentiment Analysis: A Survey,” May 2014, [Online]. Available: http://arxiv.org/abs/1405.2584
3. B. Pang and L. Lee, “Opinion Mining and Sentiment Analysis,” Foundations and Trends in Information Retrieval, vol. 2, pp. 1–135,
Apr. 2008, doi: 10.1561/1500000011.
4. S. Ali, G. Wang, and S. Riaz, “Aspect based sentiment analysis of ridesharing platform reviews for kansei engineering,” IEEE Access,
vol. 8, pp. 173186–173196, 2020, doi: 10.1109/ACCESS.2020.3025823.
5. S. Shokoohyar, “Ride-sharing platforms from drivers’ perspective: Evidence from Uber and Lyft drivers,” International Journal of
Data and Network Science, pp. 89–98, 2018, doi: 10.5267/j.ijdns.2018.10.001.
6. C. J. Hutto and E. Gilbert, “VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text,” in Proceedings
of the 8th International Conference on Weblogs and Social Media, ICWSM 2014, Apr. 2015.
7. S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Comput., vol. 9, pp. 1735–1780, Apr. 1997, doi:
10.1162/neco.1997.9.8.1735.
8. A. Virwani and S. Nanda, “AN ANALYSIS OF UBER DATA FROM A PERSPECTIVE OF CLIENT SERVICE AND REVENUE,”
International Journal of Research and Analytical Reviews, vol. 8, no. 1, 2021, [Online]. Available: www.ijrar.org
9. D. Jurafsky and J. H. Martin, “Speech and Language Processing An Introduction to Natural Language Processing, Computational
Linguistics, and Speech Recognition with Language Models Third Edition draft.”
10. B. Liu, “Sentiment Analysis and Opinion Mining,” in Synthesis Lectures on Human Language Technologies, Apr. 2012. doi:
10.2200/S00416ED1V01Y201204HLT016.
11. M. S. Ahammad, S. A. Sinthia, M. Chowdhury, N.-A.-A. Asif, and N. A. Ikram, “Sentiment Analysis of Various Ride Sharing
Applications Reviews: A Comparative Analysis between Deep Learning and Machine Learning Algorithms.”
12. A. Go, R. Bhayani, and L. Huang, “Twitter Sentiment Classification using Distant Supervision.” [Online]. Available:
http://tinyurl.com/cvvg9a
13. Y. Qixuan, “Three-Class Text Sentiment Analysis Based on LSTM,” Dec. 2024, [Online]. Available: http://arxiv.org/abs/2412.17347
14. Y. Yao and Z. Huang, “Bi-directional LSTM Recurrent Neural Network for Chinese Word Segmentation,” Feb. 2016, [Online].
Available: http://arxiv.org/abs/1602.04874
15. A. Graves, S. Fernández, and J. Schmidhuber, “Bidirectional LSTM Networks for Improved Phoneme Classification and Recognition.,”
Apr. 2005, pp. 799–804.
16. Y. Fu and C. Soman, “Real-time Data Infrastructure at Uber,” in Proceedings of the ACM SIGMOD International Conference on
Management of Data, Association for Computing Machinery, 2021, pp. 2503–2516. doi: 10.1145/3448016.3457552.
17. W. Xu et al., “An analysis of ridesharing trip time using advanced text mining techniques,” Digital Transportation and Safety, vol. 2,
no. 4, pp. 308–319, 2023, doi: 10.48130/DTS-2023-0026.18. G. Zheng et al., “Learning Phase Competition for Traffic Signal Control,” May 2019, [Online]. Available:
http://arxiv.org/abs/1905.04722
19. P. Cohen et al., “We are grateful to Using Big Data to Estimate Consumer Surplus: The Case of Uber,” 2016. [Online]. Available:
http://www.nber.org/papers/w22627
20. J. Cramer et al., “We are extremely grateful to,” 2016. [Online]. Available: http://www.nber.org/papers/w22083
21. D. Ulloa, P. Saleiro, R. J. F. Rossetti, and E. R. Silva, “Mining Social Media for Open Innovation in Transportation Systems,” Oct.
2016, [Online]. Available: http://arxiv.org/abs/1610.09894
22. L. Rayle, D. Dai, N. Chan, R. Cervero, and S. Shaheen, “Just a better taxi? A survey-based comparison of taxis, transit, and ridesourcing
services in San Francisco,” Transp. Policy (Oxf)., vol. 45, pp. 168–178, Jan. 2016, doi: 10.1016/j.tranpol.2015.10.004.
23. K. Lee, O. Levy, and L. Zettlemoyer, “Recurrent Additive Networks,” Jun. 2017, [Online]. Available: http://arxiv.org/abs/1705.07393
24. S. Gupta, R. Ranjan, and S. N. Singh, “Comprehensive Study on Sentiment Analysis: From Rule based to modern LLM based system.”
25. G. Singh, “Sentiment Analysis of Code-Mixed Social Media Text (Hinglish).”
26. A. Alamsyah, M. Jannah, and D. Ramadhani, “Uncovering Customer Issues in E-Commerce: Sentiment Analysis and Topic Modeling
Approach,” Apr. 2023, pp. 355–360. doi: 10.1109/ICOIACT59844.2023.10455920.
27. A. Alamsyah and F. Saviera, “A Comparison of Indonesia’s E-Commerce Sentiment Analysis for Marketing Intelligence Effort (case
study of Bukalapak, Tokopedia and Elevenia).”
28. Y. Chen, Q. You, J. Yuan, and J. Luo, “Twitter sentiment analysis via bi-sense emoji embedding and attention-based LSTM,” in MM
2018 - Proceedings of the 2018 ACM Multimedia Conference, Association for Computing Machinery, Inc, Oct. 2018, pp. 117–125.
doi: 10.1145/3240508.3240533.
29. X. Hu, F. Chen, C. Ma, and W. Tan, “Comment on Security and Improvement of Partial Blind Signature Scheme and Revocable
Certificateless Signature Scheme,” in Journal of Physics: Conference Series, IOP Publishing Ltd, Mar. 2021. doi: 10.1088/1742-
6596/1827/1/012005.
30. X. Zhang and Y. LeCun, “Text Understanding from Scratch,” Apr. 2016, [Online]. Available: http://arxiv.org/abs/1502.01710
31. A. Sharma and S. Dey, “Using Self-Organizing Maps for Sentiment Analysis.”
32. L. Khan, A. Amjad, N. Ashraf, and H. T. Chang, “Multi-class sentiment analysis of urdu text using multilingual BERT,” Sci. Rep., vol.
12, no. 1, Dec. 2022, doi: 10.1038/s41598-022-09381-9
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