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In the digital age, user reviews are crucial for decision-making and product development. With the growing volume of Amazon product reviews, traditional analysis methods fall short, highlighting the need for smarter, quicker, and more scalable solutions. In this paper, we present a sentiment analysis system tailored for Amazon reviews, utilizing n-grams for better context comprehension and enabling exploration of the machine learning model via real-time application. Evaluation results show model accuracy by concentrating on positive and negative reviews and fine-tuning hyperparameters.

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A Real-Time Sentiment Feedback System: Binary Categorization and Context Understanding Based on Product Reviews

  • 会议论文

  • 发表日期：

    2025年

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  • Arshpreet S. Buttar,
  • Jiawei Fan,
  • Olukoye O. Fatoki,
  • Roba Geleta,
  • Carson K. Leung

摘要

In the digital age, user reviews are crucial for decision-making and product development. With the growing volume of Amazon product reviews, traditional analysis methods fall short, highlighting the need for smarter, quicker, and more scalable solutions. In this paper, we present a sentiment analysis system tailored for Amazon reviews, utilizing n-grams for better context comprehension and enabling exploration of the machine learning model via real-time application. Evaluation results show model accuracy by concentrating on positive and negative reviews and fine-tuning hyperparameters.