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A Review on Sentiment Analysis of Product Reviews Using Machine Learning Techniques

  • Unique Paper ID: 171541
  • Volume: 11
  • Issue: 8
  • PageNo: 321-325
  • Abstract:
  • Product review sentiment analysis is vital for companies to assess customer satisfaction, pinpoint product advantages and disadvantages, and optimize marketing tactics. The proliferation of e-commerce platforms has led to an abundance of daily customer feedback, making automated sentiment classification crucial for extracting actionable insights. While traditional machine learning (ML) algorithms like Naive Bayes and Support Vector Machines (SVM) have been popular for sentiment analysis due to their efficacy in text classification, the advent of deep learning (DL) has introduced more sophisticated models. Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM) networks have demonstrated remarkable improvements in capturing intricate textual patterns and dependencies. More recently, transformer-based models such as BERT (Bidirectional Encoder Representations from Transformers) and T5 (Text-to-Text Transfer Transformer) have further advanced sentiment analysis capabilities. These models excel in accuracy, contextual understanding, and nuanced sentiment interpretation, outperforming conventional ML methods. However, challenges persist, including adapting models to specific product domains and improving DL model interpretability. Future research directions show promise in integrating explainable AI (XAI) and developing hybrid approaches that combine ML and DL to enhance model transparency and adaptability.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 8
  • PageNo: 321-325

A Review on Sentiment Analysis of Product Reviews Using Machine Learning Techniques

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Impact Factor
8.01 (Year 2024)
UGC Approved
Journal no 47859

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