survey

Sentiment Analysis for Hotel Reviews: A Systematic Literature Review

Authors:
Asma Ameur

Polytechnic School of Tunisia & LIPAH, LR111417, Tunis El Manar University, Faculty of Sciences of Tunis, Tunisia

Polytechnic School of Tunisia & LIPAH, LR111417, Tunis El Manar University, Faculty of Sciences of Tunis, Tunisia

0000-0002-2175-9310
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,
Sana Hamdi

LIPAH, LR111417, Tunis El Manar University, Faculty of Sciences of Tunis, Tunisia

LIPAH, LR111417, Tunis El Manar University, Faculty of Sciences of Tunis, Tunisia

0000-0001-6439-2275
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,
Sadok Ben Yahia

Department of Software Science, Tallinn University of Technology, Estonia

Department of Software Science, Tallinn University of Technology, Estonia

0000-0001-8939-8948
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Authors Info & Claims

ACM Computing Surveys Volume 56 Issue 2Article No.: 51pp 1–38https://doi.org/10.1145/3605152

Published:15 September 2023Publication History

ACM Computing Surveys

Abstract

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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Published in
ACM Computing Surveys Volume 56, Issue 2
February 2024
974 pages
ISSN:0360-0300
EISSN:1557-7341
DOI:10.1145/3613559
Editor:
Albert Zomaya
University of Sydney, Australia
Issue’s Table of Contents
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Publication History
- Published: 15 September 2023
- Online AM: 31 July 2023
- Accepted: 31 May 2023
- Revised: 24 March 2023
- Received: 26 April 2022
Published in csur Volume 56, Issue 2

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Hospitality industry
Sentiment Analysis
machine learning
text mining
hotel reviews
Qualifiers
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Conference
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Sentiment Analysis for Hotel Reviews: A Systematic Literature Review

ACM Computing Surveys

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Measuring Hotel Review Sentiment: An Aspect-Based Sentiment Analysis Approach

Opinion Mining on Mandalika Hotel Reviews Using Latent Dirichlet Allocation

Exploring weakly supervised latent sentiment explanations for aspect-level review analysis