Data mining techniques for tourist review classification

A large amount of information has been provided by the increasing volume of user generated content, through social networking services like reviews, comments and past experiences. Online review has become one of the most influential information sources for consumer decision-making. This information...

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Main Author:	Giro, Mustapha Abubakar
Format:	Thesis
Language:	English
Published:	2019
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/96379/1/MustaphaAbubakarGiroMSC2019.pdf.pdf
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id	my-utm-ep.96379
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spelling	my-utm-ep.963792022-07-18T10:32:44Z Data mining techniques for tourist review classification 2019 Giro, Mustapha Abubakar QA75 Electronic computers. Computer science A large amount of information has been provided by the increasing volume of user generated content, through social networking services like reviews, comments and past experiences. Online review has become one of the most influential information sources for consumer decision-making. This information is freely accessible online and used to support tourist decision-making process. Despite several studies conducted on tourist online reviews, there have been limited studies exploring tourist reviews’ ratings for 1 - 5 reviews star in predicting tourist response to an attraction. This study aims to predicting tourist ratings based on the tourist textual response (reviews) made on Petronas Twin Tower in Kuala Lumpur that is freely available on TripAdvisor. This is devised by building a predictive classification model that predicts the rating a tourist will possibly give. A qualitative approach is adopted where data miner tool was to collect tourist reviews from TripAdvisor; and the reviews dataset was preprocessed in Rapidminer to generate sentiment values which was fed to the models after some transformation. The sentiments gained/produced is utilized to compare which classification model gives the best prediction in terms of accuracy. The result showed that MLP prediction model returns a promising result in terms of accuracy over other techniques for predicting tourist response based on ratings (1-5) in which has 19% better accuracy than the other techniques tested. In conclusion, this study could contribute to the field of study by introducing a predictive model and could help destination marketers evaluate tourists’ responses to a certain destination in advance, and could also potentially influence the final destination choice by improving marketing strategies accordingly. Destinations might use these analyses to predict the weaknesses or strengths of their image based on the analysis of tourists’ reviews. 2019 Thesis http://eprints.utm.my/id/eprint/96379/ http://eprints.utm.my/id/eprint/96379/1/MustaphaAbubakarGiroMSC2019.pdf.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:143440 masters Universiti Teknologi Malaysia Faculty of Engineering - School of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Giro, Mustapha Abubakar Data mining techniques for tourist review classification
description	A large amount of information has been provided by the increasing volume of user generated content, through social networking services like reviews, comments and past experiences. Online review has become one of the most influential information sources for consumer decision-making. This information is freely accessible online and used to support tourist decision-making process. Despite several studies conducted on tourist online reviews, there have been limited studies exploring tourist reviews’ ratings for 1 - 5 reviews star in predicting tourist response to an attraction. This study aims to predicting tourist ratings based on the tourist textual response (reviews) made on Petronas Twin Tower in Kuala Lumpur that is freely available on TripAdvisor. This is devised by building a predictive classification model that predicts the rating a tourist will possibly give. A qualitative approach is adopted where data miner tool was to collect tourist reviews from TripAdvisor; and the reviews dataset was preprocessed in Rapidminer to generate sentiment values which was fed to the models after some transformation. The sentiments gained/produced is utilized to compare which classification model gives the best prediction in terms of accuracy. The result showed that MLP prediction model returns a promising result in terms of accuracy over other techniques for predicting tourist response based on ratings (1-5) in which has 19% better accuracy than the other techniques tested. In conclusion, this study could contribute to the field of study by introducing a predictive model and could help destination marketers evaluate tourists’ responses to a certain destination in advance, and could also potentially influence the final destination choice by improving marketing strategies accordingly. Destinations might use these analyses to predict the weaknesses or strengths of their image based on the analysis of tourists’ reviews.
format	Thesis
qualification_level	Master's degree
author	Giro, Mustapha Abubakar
author_facet	Giro, Mustapha Abubakar
author_sort	Giro, Mustapha Abubakar
title	Data mining techniques for tourist review classification
title_short	Data mining techniques for tourist review classification
title_full	Data mining techniques for tourist review classification
title_fullStr	Data mining techniques for tourist review classification
title_full_unstemmed	Data mining techniques for tourist review classification
title_sort	data mining techniques for tourist review classification
granting_institution	Universiti Teknologi Malaysia
granting_department	Faculty of Engineering - School of Computing
publishDate	2019
url	http://eprints.utm.my/id/eprint/96379/1/MustaphaAbubakarGiroMSC2019.pdf.pdf
_version_	1747818661949734912

Data mining techniques for tourist review classification

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