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1716739 Vol 9 · Issue 10 Download Paper

AI Customer Review Analyzer (To Generate Reports and Insights)

Manish Kumar Sharma Kashaf Ahamad Khan Ayush Pal Ishrat Ali Anuj Chandila

Subject area: Science,Engineering and Technology  ·  Area of research: Technology

DOI: 10.64388/IREV9I10-1716739

Abstract

Customer reviews play a crucial role in understanding user satisfaction and product quality. With the rapid growth of e-commerce platforms, analyzing a large volume of customer feedback manually has become impractical. This research paper presents an AI-based customer review analyzer that uses Natural Language Processing (NLP) and machine learning techniques to automatically analyze, classify, and summarize customer reviews. The system helps businesses identify customer sentiment, detect key issues, and improve decision-making efficiency.

Keywords

Customer Reviews, Artificial Intelligence, Sentiment Analysis, Natural Language Processing, Machine Learning, Opinion Mining

References

[1] B. Pang and L. Lee, “Opinion mining and sentiment analysis,” Foundations and Trends in Information Retrieval, 2008.

[2] T. Mikolov et al., “Efficient estimation of word representations in vector space,” Proc. ICLR, 2013.

[3] J. Devlin et al., “BERT: Pre-training of deep bidirectional transformers for language understanding,” Proc. NAACL, 2019.

[4] S. Liu et al., “Aspect-based sentiment analysis: A survey,” IEEE Transactions on Knowledge and Data Engineering, 2020.

[5] Y. Zhang et al., “Deep learning for sentiment analysis: A survey,” Wiley Interdisciplinary Reviews, 2018.

[6] Maas et al., “Learning word vectors for sentiment analysis,” Proc. ACL, 2011.

[7] K. Schouten and F. Frasincar, “Survey on aspect-level sentiment analysis,” IEEE Transactions on Knowledge and Data Engineering, 2016.

[8] J. Pennington, R. Socher, and C. Manning, “GloVe: Global vectors for word representation,” Proc. EMNLP, 2014.

[9] Z. Yang et al., “XLNet: Generalized autoregressive pretraining for language understanding,” Proc. NeurIPS, 2019.

[10] Y. Liu et al., “RoBERTa: A robustly optimized BERT pretraining approach,” arXiv preprint arXiv:1907.11692, 2019.

[11] N. Jindal and B. Liu, “Opinion spam and analysis,” Proc. WSDM, 2008.

[12] R. Feldman, “Techniques and applications for sentiment analysis,” Communications of the ACM, vol. 56, no. 4, pp. 82–89, 2013.

How to cite this paper

Manish Kumar Sharma, Kashaf Ahamad Khan, Ayush Pal, Ishrat Ali, Anuj Chandila "AI Customer Review Analyzer (To Generate Reports and Insights)" Iconic Research And Engineering Journals Volume 9 Issue 10 2026 Page 2990-2993 https://doi.org/10.64388/IREV9I10-1716739
Manish Kumar Sharma, Kashaf Ahamad Khan, Ayush Pal, Ishrat Ali, Anuj Chandila "AI Customer Review Analyzer (To Generate Reports and Insights)" Iconic Research And Engineering Journals, vol. 9, no. 10, Apr. 2026, doi: https://doi.org/10.64388/IREV9I10-1716739
Manish Kumar Sharma, Kashaf Ahamad Khan, Ayush Pal, Ishrat Ali, Anuj Chandila (2026). AI Customer Review Analyzer (To Generate Reports and Insights). Iconic Research And Engineering Journals, 9(10). doi: https://doi.org/10.64388/IREV9I10-1716739
Manish Kumar Sharma, Kashaf Ahamad Khan, Ayush Pal, Ishrat Ali, Anuj Chandila "AI Customer Review Analyzer (To Generate Reports and Insights)" Iconic Research And Engineering Journals, vol. 9, no. 10, Apr. 2026. Crossref, https://doi.org/10.64388/IREV9I10-1716739
@article{1716739,
      author = {Manish Kumar Sharma, Kashaf Ahamad Khan, Ayush Pal, Ishrat Ali, Anuj Chandila},
      title = {AI Customer Review Analyzer (To Generate Reports and Insights)},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {10},
      pages = {2990-2993},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1716739.pdf},
      abstract = {Customer reviews play a crucial role in understanding user satisfaction and product quality. With the rapid growth of e-commerce platforms, analyzing a large volume of customer feedback manually has become impractical. This research paper presents an AI-based customer review analyzer that uses Natural Language Processing (NLP) and machine learning techniques to automatically analyze, classify, and summarize customer reviews. The system helps businesses identify customer sentiment, detect key issues, and improve decision-making efficiency.},
      keywords = {Customer Reviews, Artificial Intelligence, Sentiment Analysis, Natural Language Processing, Machine Learning, Opinion Mining},
      month = {April},
      doi = {https://doi.org/10.64388/IREV9I10-1716739}
  }