In today’s digital background, sentiment analysis has become an essential factor of Natural Language Processing (NLP), offering valuable insights from vast online data sources. This paper presents a comparative analysis of sentiment analysis techniques leveraging machine learning. As digital content continues to expand rapidly, decoding public sentiment has become increasingly important for businesses and researchers. The study examines various approaches, including traditional machine learning methods like Support Vector Machines (SVM) and Na？ve Bayes (NB), as well as deep learning models such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. It also explores hybrid frameworks that combine the strength of both paradigms. By evaluating the advantages and limitations of these models, especially within the context of e-commerce, this review provides a comprehensive understanding of their performance. Additionally, the paper addresses critical challenges such as real-time sentiment detection and multi-label classification. Through a synthesis of existing research, it highlights promising directions for future work and contributes to the development of more accurate and practical sentiment analysis solutions across various applications.
References
[1]
Pang, B. and Lee, L. (2008) Opinion Mining and Sentiment Analysis. Foundations and Trends® in Information Retrieval, 2, 1-135. https://doi.org/10.1561/1500000011
[2]
Liu, B. (2022) Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers.
[3]
Wankhade, M., Rao, A.C.S. and Kulkarni, C. (2022) A Survey on Sentiment Analysis Methods, Applications, and Challenges. Artificial Intelligence Review, 55, 5731-5780. https://doi.org/10.1007/s10462-022-10144-1
[4]
Sharma, N.A., Ali, A.B.M.S. and Kabir, M.A. (2025) A Review of Sentiment Analysis: Tasks, Applications, and Deep Learning Techniques. International Journal of Data Science and Analytics, 19, 351-388. https://doi.org/10.1007/s41060-024-00594-x
[5]
Ahmad, M., Aftab, S., Ali, I. and Hameed, N. (2017) Hybrid Tools and Techniques for Sentiment Analysis: A Review. International Journal of Multidisciplinary Sciences and Engineering, 8, 28-33.
[6]
Yue, L., Chen, W., Li, X., Zuo, W. and Yin, M. (2019) A Survey of Sentiment Analysis in Social Media. Knowledge and Information Systems, 60, 617-663. https://doi.org/10.1007/s10115-018-1236-4
[7]
Suryawanshi, N.S. (2024) Sentiment Analysis with Machine Learning and Deep Learning: A Survey of Techniques and Applications. International Journal of Science and Research Archive, 12, 5-15. https://doi.org/10.30574/ijsra.2024.12.2.1205
[8]
Devlin, J., Chang, M.-W., Lee, K. and Toutanova, K. (2019) BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT 2019, Minneapolis, 2-7 June 2019, 4171-4186.
[9]
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C.D., Ng, A., et al. (2013) Recursive Deep Models for Semantic Compositionality over a Sentiment Treebank. Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, Seattle, 18-21 October 2013, 1631-1642. https://doi.org/10.18653/v1/d13-1170
[10]
Feldman, R. (2013) Techniques and Applications for Sentiment Analysis: The Main Applications and Challenges of One of the Hottest Research Areas in Computer Science. Communications of the ACM, 56, 82-89. https://doi.org/10.1145/2436256.2436274
[11]
Shathik, A. and Prasad, K. (2020) A Literature Review on Application of Sentiment Analysis Using Machine Learning Techniques. International Journal of Applied Engineering and Management Letters (IJAEML): A Refereed International Journal of Srinivas University, 4, 2581-7000.
[12]
Chaturvedi, S., Mishra, V. and Mishra, N. (2017) Sentiment Analysis Using Machine Learning for Business Intelligence. 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), Chennai, 21-22 September 2017, 2162-2166. https://doi.org/10.1109/icpcsi.2017.8392100
[13]
Huang, H., Zavareh, A.A. and Mustafa, M.B. (2023) Sentiment Analysis in E-Commerce Platforms: A Review of Current Techniques and Future Directions. IEEE Access, 11, 90367-90382. https://doi.org/10.1109/access.2023.3307308
[14]
Alonso, M.A., Vilares, D., Gómez-Rodríguez, C. and Vilares, J. (2021) Sentiment Analysis for Fake News Detection. Electronics, 10, Article 1348. https://doi.org/10.3390/electronics10111348
[15]
Kawade, D.R. and Oza, D.K.S. (2017) Sentiment Analysis: Machine Learning Approach. International Journal of Engineering and Technology, 9, 2183-2186. https://doi.org/10.21817/ijet/2017/v9i3/1709030151
[16]
Yi, S. and Liu, X. (2020) Machine Learning Based Customer Sentiment Analysis for Recommending Shoppers, Shops Based on Customers’ Review. Complex & Intelligent Systems, 6, 621-634. https://doi.org/10.1007/s40747-020-00155-2
[17]
Deniz, E., Erbay, H. and Coşar, M. (2022) Multi-Label Classification of E-Commerce Customer Reviews via Machine Learning. Axioms, 11, Article 436. https://doi.org/10.3390/axioms11090436
[18]
Saura, J.R., Palos-Sanchez, P. and Grilo, A. (2019) Detecting Indicators for Startup Business Success: Sentiment Analysis Using Text Data Mining. Sustainability, 11, Article 917. https://doi.org/10.3390/su11030917
[19]
Alnasrawi, A.M., Alzubaidi, A.M.N. and Al-Moadhen, A.A. (2024) Improving Sentiment Analysis Using Text Network Features within Different Machine Learning Algorithms. Bulletin of Electrical Engineering and Informatics, 13, 405-412. https://doi.org/10.11591/eei.v13i1.5576
[20]
Mehta, K. and Panda, S.P. (2022) Sentiment Analysis on E-Commerce Apparels Using Convolutional Neural Network. International Journal of Computing, 21, 234-241. https://doi.org/10.47839/ijc.21.2.2592
[21]
Lin, X. (2020) Sentiment Analysis of E-Commerce Customer Reviews Based on Natural Language Processing. Proceedings of the 2020 2nd International Conference on Big Data and Artificial Intelligence, Johannesburg, 28-30 April 2020, 32-36. https://doi.org/10.1145/3436286.3436293
[22]
Kundra, H., Vishnu, C., Vaishnavi, B.V., Kasireddy, C.R., Akash, G. and Jindam, S. (2024) Digital Emotions Using Sentiment Analysis for Predictive Insights on Customer Recommendations. 2024 IEEE 5th India Council International Subsections Conference (INDISCON), Chandigarh, 22-24 August 2024, 1-6. https://doi.org/10.1109/indiscon62179.2024.10744376
[23]
Zhao, H., Liu, Z., Yao, X. and Yang, Q. (2021) A Machine Learning-Based Sentiment Analysis of Online Product Reviews with a Novel Term Weighting and Feature Selection Approach. Information Processing & Management, 58, Article ID: 102656. https://doi.org/10.1016/j.ipm.2021.102656
[24]
Vatambeti, R., Mantena, S.V., Kiran, K.V.D., Manohar, M. and Manjunath, C. (2024) Twitter Sentiment Analysis on Online Food Services Based on Elephant Herd Optimization with Hybrid Deep Learning Technique. Cluster Computing, 27, 655-671. https://doi.org/10.1007/s10586-023-03970-7
[25]
Behera, R.K., Jena, M., Rath, S.K. and Misra, S. (2021) Co-LSTM: Convolutional LSTM Model for Sentiment Analysis in Social Big Data. Information Processing & Management, 58, Article ID: 102435. https://doi.org/10.1016/j.ipm.2020.102435
[26]
Ray, P. and Chakrabarti, A. (2020) A Mixed Approach of Deep Learning Method and Rule-Based Method to Improve Aspect Level Sentiment Analysis. Applied Computing and Informatics, 18, 163-178. https://doi.org/10.1016/j.aci.2019.02.002
[27]
Jain, P.K., Saravanan, V. and Pamula, R. (2021) A Hybrid CNN-LSTM: A Deep Learning Approach for Consumer Sentiment Analysis Using Qualitative User-Generated Contents. ACM Transactions on Asian and Low-Resource Language Information Processing, 20, 1-15. https://doi.org/10.1145/3457206
[28]
Priyadarshini, I. and Cotton, C. (2021) A Novel LSTM-CNN-Grid Search-Based Deep Neural Network for Sentiment Analysis. The Journal of Supercomputing, 77, 13911-13932. https://doi.org/10.1007/s11227-021-03838-w
[29]
Jabbar, J., Urooj, I., JunSheng, W. and Azeem, N. (2019) Real-Time Sentiment Analysis on E-Commerce Application. 2019 IEEE 16th International Conference on Networking, Sensing and Control (ICNSC), Banff, 9-11 May 2019, 391-396. https://doi.org/10.1109/icnsc.2019.8743331
[30]
Li, Y., Chan, J., Peko, G. and Sundaram, D. (2024) An Explanation Framework and Method for AI-Based Text Emotion Analysis and Visualisation. Decision Support Systems, 178, Article ID: 114121. https://doi.org/10.1016/j.dss.2023.114121
