Modeling tourism movements and sentiments using spatial analysis based on social media big data

Tourism can greatly meet people's spiritual needs. Due to the good interactivity and timeliness of social media, people are more willing to share their travel through social media platforms. This thesis constructs a model using Sina Weibo data to examine the attention and sentiment of Chinese t...

Full description

Saved in:

Bibliographic Details
Main Author:	Zhu, Chen
Format:	Thesis
Language:	English English
Published:	2023
Subjects:	G154.9-155.8 Travel and state Tourism
Online Access:	https://eprints.ums.edu.my/id/eprint/39097/1/24%20PAGES..pdf https://eprints.ums.edu.my/id/eprint/39097/2/FULLTEXT..pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-ums-ep.39097
record_format	uketd_dc
spelling	my-ums-ep.390972024-07-16T07:42:48Z Modeling tourism movements and sentiments using spatial analysis based on social media big data 2023 Zhu, Chen G154.9-155.8 Travel and state. Tourism Tourism can greatly meet people's spiritual needs. Due to the good interactivity and timeliness of social media, people are more willing to share their travel through social media platforms. This thesis constructs a model using Sina Weibo data to examine the attention and sentiment of Chinese tourists visiting Sabah, Malaysia. A correlation analysis was conducted between the number of inbound Chinese tourists and Sina Weibo data to verify the validity of Sina Weibo data. Research objectives include: 1. Design an algorithm to collect data from social media platforms and build a database to store and manage social media data. 2. Implement spatial analysis methods to discover hotspot areas of tourist attention and visualize their shape, size and distribution. 3. Implement natural language processing methods to analyze tourists’ emotions from text data.The methods used in this thesis include web crawler technology, spatial analysis methods and sentiment analysis. The correlation analysis results show that Sina Weibo data is positively correlated with the number of Chinese tourists, indicating that Sina Weibo data can reflect changes in the number of Chinese tourists.The differences in the distribution of Sabah tourist destinations are more obvious, and the distribution balance is low. Chinese tourists mainly come from Guangdong, Zhejiang and Beijing. The average sentiment score from Beijing is higher, and Zhejiang is lower. On a large scale, Chinese tourists’ attention is mainly concentrated on Kota Kinabalu and Semporna. The number of destinations with positive sentiments showed an upward trend in Sabah, negative sentiment trended downward. In 2016, tourist attention shifted from Semporna to Kota Kinabalu. The spatial distribution of tourist destinations shows an east-west direction, and the attention shows a northwestsoutheast direction. On a small scale, the attention is mainly concentrated in the southwest area of Kota Kinabalu. The average sentiment of Chinese tourists was positive, showing an upward trend, while negative sentiment showed a downward trend. In 2017, tourist attention shifted from urban to the southeast of urban. Kota Kinabalu’s tourist destinations and attention are distributed in the southwestnortheast direction. The average sentiment score in Qinghai is negative. Tourist attention and sentiment analysis based on social media big data will support decision-makers to plan a comprehensive framework for Sabah tourism, so that more areas in Sabah can enjoy the economic growth brought by tourism. 2023 Thesis https://eprints.ums.edu.my/id/eprint/39097/ https://eprints.ums.edu.my/id/eprint/39097/1/24%20PAGES..pdf text en public https://eprints.ums.edu.my/id/eprint/39097/2/FULLTEXT..pdf text en validuser dphil doctoral Universiti Malaysia Sabah Faculty of Social Sciences and Humanities
institution	Universiti Malaysia Sabah
collection	UMS Institutional Repository
language	English English
topic	G154.9-155.8 Travel and state Tourism
spellingShingle	G154.9-155.8 Travel and state Tourism Zhu, Chen Modeling tourism movements and sentiments using spatial analysis based on social media big data
description	Tourism can greatly meet people's spiritual needs. Due to the good interactivity and timeliness of social media, people are more willing to share their travel through social media platforms. This thesis constructs a model using Sina Weibo data to examine the attention and sentiment of Chinese tourists visiting Sabah, Malaysia. A correlation analysis was conducted between the number of inbound Chinese tourists and Sina Weibo data to verify the validity of Sina Weibo data. Research objectives include: 1. Design an algorithm to collect data from social media platforms and build a database to store and manage social media data. 2. Implement spatial analysis methods to discover hotspot areas of tourist attention and visualize their shape, size and distribution. 3. Implement natural language processing methods to analyze tourists’ emotions from text data.The methods used in this thesis include web crawler technology, spatial analysis methods and sentiment analysis. The correlation analysis results show that Sina Weibo data is positively correlated with the number of Chinese tourists, indicating that Sina Weibo data can reflect changes in the number of Chinese tourists.The differences in the distribution of Sabah tourist destinations are more obvious, and the distribution balance is low. Chinese tourists mainly come from Guangdong, Zhejiang and Beijing. The average sentiment score from Beijing is higher, and Zhejiang is lower. On a large scale, Chinese tourists’ attention is mainly concentrated on Kota Kinabalu and Semporna. The number of destinations with positive sentiments showed an upward trend in Sabah, negative sentiment trended downward. In 2016, tourist attention shifted from Semporna to Kota Kinabalu. The spatial distribution of tourist destinations shows an east-west direction, and the attention shows a northwestsoutheast direction. On a small scale, the attention is mainly concentrated in the southwest area of Kota Kinabalu. The average sentiment of Chinese tourists was positive, showing an upward trend, while negative sentiment showed a downward trend. In 2017, tourist attention shifted from urban to the southeast of urban. Kota Kinabalu’s tourist destinations and attention are distributed in the southwestnortheast direction. The average sentiment score in Qinghai is negative. Tourist attention and sentiment analysis based on social media big data will support decision-makers to plan a comprehensive framework for Sabah tourism, so that more areas in Sabah can enjoy the economic growth brought by tourism.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Zhu, Chen
author_facet	Zhu, Chen
author_sort	Zhu, Chen
title	Modeling tourism movements and sentiments using spatial analysis based on social media big data
title_short	Modeling tourism movements and sentiments using spatial analysis based on social media big data
title_full	Modeling tourism movements and sentiments using spatial analysis based on social media big data
title_fullStr	Modeling tourism movements and sentiments using spatial analysis based on social media big data
title_full_unstemmed	Modeling tourism movements and sentiments using spatial analysis based on social media big data
title_sort	modeling tourism movements and sentiments using spatial analysis based on social media big data
granting_institution	Universiti Malaysia Sabah
granting_department	Faculty of Social Sciences and Humanities
publishDate	2023
url	https://eprints.ums.edu.my/id/eprint/39097/1/24%20PAGES..pdf https://eprints.ums.edu.my/id/eprint/39097/2/FULLTEXT..pdf
_version_	1804890372185260032

Modeling tourism movements and sentiments using spatial analysis based on social media big data

Similar Items