Unveiling Customer Sentiments: Advanced Machine Learning Techniques for Analyzing Reviews

References

[1] W. Medhat, A. Hassan, and H. Korashy, “Sentiment analysis algorithms and applications: A survey,” Ain Shams Engineering Journal, vol. 5, no. 4, pp. 1093–1113, 2014.

[2] B. Pang and L. Lee, “Opinion mining and sentiment analysis,” Founda- tions and Trends in Information Retrieval, vol. 2, no. 1–2, pp. 1–135, 2008.

[3] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre- training of deep bidirectional transformers for language understanding,” in Proceedings of NAACL-HLT, 2019, pp. 4171–4186.

[4] F. Koto, G. Xia, J. Liu, and E. Lau, “IndoLEM and IndoBERT: A benchmark dataset and pre-trained language model for Indonesian NLP,” in Proceedings of the 28th International Conference on Computational Linguistics, 2020, pp. 757–770.

[5] A. Yadav and S. Vishwakarma, “Sentiment analysis using deep learning architectures: A review,” Artificial Intelligence Review, vol. 54, no. 7, pp. 4827–4860, 2021.

[6] B. Liu, Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers, 2012.

[7] L. Zhang, S. Wang, and B. Liu, “Deep learning for sentiment analysis: A survey,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 8, no. 4, p. e1253, 2018.

[8] K. Schouten and F. Frasincar, “Survey on aspect-level sentiment analy- sis,” IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 3, pp. 813-830, 2016.

[9] M. Pontiki et al., “Semeval-2016 task 5: Aspect-based sentiment analy- sis,” in Proceedings of SemEval, 2016.

[10] H. Xu et al., “BERT Post-Training for Aspect-Based Sentiment Analy- sis,” in Proceedings of NAACL, 2019.

[11] B. Liu, Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers, 2012.

[12] B. Pang and L. Lee, “Opinion mining and sentiment analysis,” Founda- tions and Trends in Information Retrieval, vol. 2, no. 1–2, pp. 1–135, 2008.

[13] M. Pontiki et al., “Semeval-2016 task 5: Aspect-based sentiment analy- sis,” in Proceedings of SemEval, 2016.

[14] H. Xu et al., “BERT Post-Training for Aspect-Based Sentiment Analy- sis,” in Proceedings of NAACL, 2019.

[15] F. Koto, G. Xia, J. Liu, and E. Lau, “IndoLEM and IndoBERT: A benchmark dataset and pre-trained language model for Indonesian NLP,” in Proceedings of the 28th International Conference on Computational Linguistics, 2020, pp. 757–770.

[16] A. Yadav and S. Vishwakarma, “Sentiment analysis using deep learning architectures: A review,” Artificial Intelligence Review, vol. 54, no. 7, pp. 4827–4860, 2021.

[17] Myakala, P. K. (2024). Consciousness in Machines: A Critical Explo- ration. INTERNATIONAL JOURNAL OF MULTIDISCIPLINARY RE- SEARCH AND ANALYSIS, 7(12), 10-47191.

@article{1707104,
      author = {Srikanth Kamatala, Chiranjeevi Bura, Anil Kumar Jonnalagadda},
      title = {Unveiling Customer Sentiments: Advanced Machine Learning Techniques for Analyzing Reviews},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {8},
      pages = {123-129},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1707104.pdf},
      abstract = {Customer reviews of tools and manufacturing prod- ucts provide valuable insights into product performance, durabil- ity, and overall user satisfaction. However, extracting meaningful information from vast amounts of unstructured review data remains a challenge. This study explores advanced machine learn- ing techniques for sentiment analysis specifically applied to tools and manufacturing product reviews. It evaluates the effectiveness of various machine learning and deep learning models, including Support Vector Machines (SVM), Random Forest, Na??ve Bayes, Long Short-Term Memory (LSTM), and Transformer-based architectures like BERT. The research emphasizes key challenges unique to this domain, such as interpreting technical jargon, handling mixed sentiments in product reviews, and differentiating between subjective opinions and objective performance assessments. Additionally, the study examines the role of domain- specific word embeddings and fine-tuned language models in improving sentiment classification accuracy. Using real-world datasets from e-commerce platforms and manufacturing product reviews, this research provides empirical insights into the best- performing models for sentiment analysis in this niche. The findings highlight the potential of deep learning techniques in automating sentiment classification, assisting manufacturers in quality control, product development, and customer satisfaction analysis. This study contributes to AI-driven sentiment analysis research by offering domain-specific recommendations for businesses looking to leverage machine learning for analyzing customer feedback in the tools and manufacturing sector.},
      keywords = {Customer Product Reviews, Sentiment analysis},
      month = {February},
  }