review · ACM Computing Surveys
Sentiment analysis allows automated extraction of customer satisfaction levels from hotel guest experiences and feedback shared across social media. While numerous investigations aim to enhance the precision of sentiment analysis within the hospitality sector, these efforts differ substantially across several technical dimensions. Key differences appear in data preprocessing methods, feature representation approaches, sentiment classification levels, analytical models, and the datasets utilised. Monitoring and understanding these technical variations is necessary to advance the field. Although sentiment analysis plays an important role in hospitality and tourism research, systematic evaluations that pinpoint research gaps and outline future directions have remained scarce. A comprehensive review addresses this deficit by surveying and categorising current state-of-the-art literature focused on hotel review sentiment analysis, mapping out how different computational techniques are currently implemented.
Online reviews directly reflect customer satisfaction, making automated sentiment tracking valuable for the hospitality sector. Clarifying how different computational tools evaluate customer opinions helps both researchers and practitioners understand which methodologies are being used to interpret guest feedback, identifying areas where sentiment analysis techniques can be strengthened.
The work focuses on categorising existing academic techniques rather than introducing a ready product. In commercial settings, sentiment analysis tools enable hotel operators and hospitality platforms to track customer satisfaction and brand reputation from social media reviews. Because this work constitutes a secondary literature review surveying existing methodologies, it represents an early-stage conceptual resource that maps available approaches rather than a tested, deployable software solution.
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Sentiment Analysis (SA) helps to automatically and meaningfully discover hotel customers’ satisfaction from their shared experiences and feelings on social media. Several studies have been conducted to improve the precision of SA in the hospitality industry, which vary in data preprocessing techniques, feature representation, sentiment classification levels, and models, and they use different datasets. Such variations are worthy of attention and monitoring. Despite the importance of SA in hospitality and tourism, review studies identifying gaps and suggesting future research directions are limited. This article introduces a systematic literature review to label and discuss state-of-the-art studies that deal with SA for hotel reviews.
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DOI: 10.1145/3605152
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