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Unveiling Customer Sentiments: Advanced Machine Learning Techniques for Analyzing Reviews
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
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
How to cite this paper
@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},
}