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Organizations such as hotels can utilize AI to analyze reviews cost-effectively, thereby eliminating the need for customer service experts. Deep learning (DL) methods, while accurate with large datasets, often lack explainability. This paper utilizes a Bi-LSTM for sentiment analysis on a collection of 515,000 hotel reviews, achieving an accuracy of 89.67%. We then apply both SHAP and LIME, two prominent but different XAI tools, to explain our model. These techniques clarify how specific words impact sentiment, validating and interpreting the Bi-LSTM model’s decisions.

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Using Explainable AI (XAI) to Understand Sentiment Analysis of Hotel Reviews

  • 会议论文

  • 发表日期：

    2025年

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  • Christopher G. Harris

摘要

Organizations such as hotels can utilize AI to analyze reviews cost-effectively, thereby eliminating the need for customer service experts. Deep learning (DL) methods, while accurate with large datasets, often lack explainability. This paper utilizes a Bi-LSTM for sentiment analysis on a collection of 515,000 hotel reviews, achieving an accuracy of 89.67%. We then apply both SHAP and LIME, two prominent but different XAI tools, to explain our model. These techniques clarify how specific words impact sentiment, validating and interpreting the Bi-LSTM model’s decisions.