AI-Driven Sentiment Analysis for Enhancing Customer Experience in E-Commerce

Alladi Deekshith

Abstract


Understanding customer sentiment is vital for improving e-commerce experiences. This paper presents an AI-driven sentiment analysis framework that processes customer reviews and feedback to identify trends and preferences. The system employs Natural Language Processing (NLP) techniques and transformer-based models to analyze text data with high precision. Insights derived from the analysis enable businesses to tailor their offerings, enhance customer satisfaction, and boost sales. Case studies on e-commerce platforms validate the effectiveness of the proposed framework in driving customer-centric decision-making.

References


Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems (pp. 1097-1105).

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.

Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. In Proceedings of the International Conference on Learning Representations (ICLR).

Murphy, K. P. (2012). Machine learning: A probabilistic perspective. MIT Press.

Ng, A. Y., & Jordan, M. I. (2002). On discriminative vs. generative classifiers: A comparison of logistic regression and naive Bayes. In Advances in Neural Information Processing Systems (pp. 841-848).

Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533-536.

Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85-117.

Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., ... & Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484-489.

Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). MIT Press.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems (pp. 5998-6008).

Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600-612.

Williams, R. J. (1992). Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8(3-4), 229-256.

Zhou, Z. H. (2012). Ensemble methods: Foundations and algorithms. Chapman & Hall/CRC.

Raghunath, V., Kunkulagunta, M., & Nadella, G. S. (2021). Leveraging Cloud Computing for Efficient Data Processing in SAP Enterprise Solutions. International Journal of Machine Learning for Sustainable Development, 3(4).

Raghunath, V., Kunkulagunta, M., & Nadella, G. S. (2021). Machine Learning in SAP Workflows: A Study of Predictive Analytics and Automation. Transactions on Latest Trends in Artificial Intelligence, 2(2).

Raghunath, V., Kunkulagunta, M., & Nadella, G. S. (2021). Machine Learning Models for Optimizing SAP-Based Data Processing in Cloud Environments. International Journal of Sustainable Development in Computing Science, 3(3).

Ranjan, P., & Dahiya, S. (2021). Advanced threat detection in api security: Leveraging machine learning algorithms. International Journal of Communication Networks and Information Security, 13(1).

Dhaiya, S., Pandey, B. K., Adusumilli, S. B. K., & Avacharmal, R. (2021) Optimizing API Security in FinTech Through Genetic Algorithm based Machine Learning Model.

Chintale, P., Korada, L., Ranjan, P., & Malviya, R. K. (2019). Adopting Infrastructure as Code (IaC) for Efficient Financial Cloud Management. ISSN: 2096-3246, 51(04).

Raghunath, V., Kunkulagunta, M., & Nadella, G. S. (2020). Optimizing SAP Data Processing with Machine Learning Algorithms in Cloud Environments. International Transactions in Artificial Intelligence, 4(4).

Raghunath, V., Kunkulagunta, M., & Nadella, G. S. (2020). Artificial Intelligence in Business Analytics: Cloud-Based Strategies for Data Processing and Integration. International Journal of Sustainable Development in Computing Science, 2(4).

Raghunath, V., Kunkulagunta, M., & Nadella, G. S. (2020). Scalable Data Processing Pipelines: The Role of AI and Cloud Computing. International Scientific Journal for Research, 2(2).


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