research-article

Ranking Online User Reviews for Tourism Based on Usefulness

Authors:

Konstantinos Tsamis,

Andreas Komninos,

Konstantinos Kovas,

Nikolaos ZotosAuthors Info & Claims

PCI '20: Proceedings of the 24th Pan-Hellenic Conference on Informatics

Pages 208 - 213

https://doi.org/10.1145/3437120.3437308

Published: 04 March 2021 Publication History

Abstract

The growth of Web 2.0 services has lead to an increase in the volume of user-generated content in the form of online user reviews. Web platforms offering users the ability to evaluate the services of hotels have increased in popularity, as a large percentage of travellers offer their feedback or read hotel reviews to assist their decision making process. Users usually do not have time to go through the sheer volume of available hotel reviews and would prefer to read the most useful ones, whereas review usefulness is subjective and depends on the reader’s needs and preferences. Therefore, the need for automatically detecting hotel review helpfulness arises.

In this paper, we propose the use of features that capture both textual content and review metadata for predicting hotel review helpfulness of Greek and English reviews. A novel approach for representing text as a word embeddings-based vector is introduced and review association with certain hotel service aspects is mapped. Evaluating the performance of our approach using Machine Learning and Neural classifiers yields promising results for the review helpfulness classification task.

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Digital Library

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Cited By

Luo JZhang YGao YZhang J(2023)A novel method based on knowledge adoption model and non-kernel SVM for predicting the helpfulness of online reviewsJournal of the Operational Research Society10.1080/01605682.2023.2239855(1-18)Online publication date: 28-Jul-2023
https://doi.org/10.1080/01605682.2023.2239855

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cover image ACM Other conferences

PCI '20: Proceedings of the 24th Pan-Hellenic Conference on Informatics

November 2020

433 pages

ISBN:9781450388979

DOI:10.1145/3437120

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 March 2021

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Hellenic Government and European Regional Development Fund

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PCI 2020

PCI 2020: 24th Pan-Hellenic Conference on Informatics

November 20 - 22, 2020

Athens, Greece

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Overall Acceptance Rate 190 of 390 submissions, 49%

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Cited By

Luo JZhang YGao YZhang J(2023)A novel method based on knowledge adoption model and non-kernel SVM for predicting the helpfulness of online reviewsJournal of the Operational Research Society10.1080/01605682.2023.2239855(1-18)Online publication date: 28-Jul-2023
https://doi.org/10.1080/01605682.2023.2239855

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