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As shopping shifts to the online domain, challenges arise. Consumers rely on online reviews, which play a crucial role in helping them make informed purchases. Customer feedback also serves as valuable market research. Manual analysis of reviews is inefficient. Artificial intelligence (AI) can streamline this process, yielding accurate results. Due to the evolving nature of language, traditional numerical machine learning models struggle with textual analysis, making natural language processing (NLP) a more effective approach. Bidirectional encoder representations from transformers (BERT) offers a method for encoding and analyzing text. This work tests how useful a proposed NLP-based approach using BERT is for evaluating whether customer feedback in product reviews is positive or negative. However, as performance of classifiers is coupled with proper parameter selection, a modified version of a optimization metaheuristics, specifically the firefly algorithm (FA), is introduced to help improve accuracy through parameter tuning. The best-performing models demonstrate admirable outcomes with sentiment classification accuracy being 0.868961.

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Optimized Classifying of User Review Sentiment via Modified Metaheuristic

  • 会议论文

  • 发表日期：

    2025年

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  • Luka Jovanovic,
  • Stanislava Kozakijevic,
  • Milos Antonijevic,
  • Marina Milovanovic,
  • Milos Mravik,
  • Miodrag Zivkovic,
  • Nebojsa Bacanin

摘要

As shopping shifts to the online domain, challenges arise. Consumers rely on online reviews, which play a crucial role in helping them make informed purchases. Customer feedback also serves as valuable market research. Manual analysis of reviews is inefficient. Artificial intelligence (AI) can streamline this process, yielding accurate results. Due to the evolving nature of language, traditional numerical machine learning models struggle with textual analysis, making natural language processing (NLP) a more effective approach. Bidirectional encoder representations from transformers (BERT) offers a method for encoding and analyzing text. This work tests how useful a proposed NLP-based approach using BERT is for evaluating whether customer feedback in product reviews is positive or negative. However, as performance of classifiers is coupled with proper parameter selection, a modified version of a optimization metaheuristics, specifically the firefly algorithm (FA), is introduced to help improve accuracy through parameter tuning. The best-performing models demonstrate admirable outcomes with sentiment classification accuracy being 0.868961.