Using Geotagged Social Media Data to Explore Sentiment Changes in Tourist Flow: A Spatiotemporal Analytical Framework

<b> </b>Understanding sentiment changes in tourist flow is critical in designing exciting experiences for tourists and promoting sustainable tourism development. This paper proposes a novel analytical framework to investigate the tourist sentiment changes between different attractions ba...

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Main Authors: Wei Jiang, Zhengan Xiong, Qin Su, Yi Long, Xiaoqing Song, Peng Sun
Format: Article
Language:English
Published: MDPI AG 2021-03-01
Series:ISPRS International Journal of Geo-Information
Subjects:
Online Access:https://www.mdpi.com/2220-9964/10/3/135
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spelling doaj-6cd106d6eb334082b5cfbf87a75fcf752021-03-04T00:07:26ZengMDPI AGISPRS International Journal of Geo-Information2220-99642021-03-011013513510.3390/ijgi10030135Using Geotagged Social Media Data to Explore Sentiment Changes in Tourist Flow: A Spatiotemporal Analytical FrameworkWei Jiang0Zhengan Xiong1Qin Su2Yi Long3Xiaoqing Song4Peng Sun5School of Geography and Tourism, Anhui Normal University, Wuhu 241003, ChinaSchool of Geography and Tourism, Anhui Normal University, Wuhu 241003, ChinaSchool of Geography and Tourism, Anhui Normal University, Wuhu 241003, ChinaKey Laboratory of Virtual Geographic Environment, Nanjing Normal University, Nanjing 210023, ChinaKey Laboratory of Virtual Geographic Environment, Nanjing Normal University, Nanjing 210023, ChinaSchool of Geography and Tourism, Anhui Normal University, Wuhu 241003, China<b> </b>Understanding sentiment changes in tourist flow is critical in designing exciting experiences for tourists and promoting sustainable tourism development. This paper proposes a novel analytical framework to investigate the tourist sentiment changes between different attractions based on geotagged social media data. Our framework mainly focuses on visualizing the detailed sentiment changes of tourists and exploring the valuable spatiotemporal pattern of the sentiment changes in tourist flow. The tourists were first identified from social media users. Then, we accurately evaluated the tourist sentiment by constructing a Chinese sentiment dictionary, grammatical rule, and sentiment score. Based on the location information of social media data, we built and visualized the tourist flow network. Last, to further reveal the impact of attractions on the sentiment of tourist flow, the positive and negative sentiment profiles were generated by mining social media texts. We took Beijing, a famous tourist destination in China, as a case study. Our results revealed the following: (1) the temporal trend of tourist sentiment has seasonal characteristics and is significantly influenced by government control policies against COVID-19; (2) due to the impact of the attraction’s historical background, some tourist flows with highly decreased sentiment strength are linked to attractions; (3) on the long journey to the attraction, the sentiment strength of tourists decreases; and (4) bad traffic conditions can significantly decrease tourist sentiment. This study highlights the methodological implications of visualizing sentiment changes during collective tourist movement and provides comprehensive insight into the spatiotemporal pattern of tourist sentiment.https://www.mdpi.com/2220-9964/10/3/135tourist flowsentiment changespatiotemporal analysisgeotagged social media data
collection DOAJ
language English
format Article
sources DOAJ
author Wei Jiang
Zhengan Xiong
Qin Su
Yi Long
Xiaoqing Song
Peng Sun
spellingShingle Wei Jiang
Zhengan Xiong
Qin Su
Yi Long
Xiaoqing Song
Peng Sun
Using Geotagged Social Media Data to Explore Sentiment Changes in Tourist Flow: A Spatiotemporal Analytical Framework
ISPRS International Journal of Geo-Information
tourist flow
sentiment change
spatiotemporal analysis
geotagged social media data
author_facet Wei Jiang
Zhengan Xiong
Qin Su
Yi Long
Xiaoqing Song
Peng Sun
author_sort Wei Jiang
title Using Geotagged Social Media Data to Explore Sentiment Changes in Tourist Flow: A Spatiotemporal Analytical Framework
title_short Using Geotagged Social Media Data to Explore Sentiment Changes in Tourist Flow: A Spatiotemporal Analytical Framework
title_full Using Geotagged Social Media Data to Explore Sentiment Changes in Tourist Flow: A Spatiotemporal Analytical Framework
title_fullStr Using Geotagged Social Media Data to Explore Sentiment Changes in Tourist Flow: A Spatiotemporal Analytical Framework
title_full_unstemmed Using Geotagged Social Media Data to Explore Sentiment Changes in Tourist Flow: A Spatiotemporal Analytical Framework
title_sort using geotagged social media data to explore sentiment changes in tourist flow: a spatiotemporal analytical framework
publisher MDPI AG
series ISPRS International Journal of Geo-Information
issn 2220-9964
publishDate 2021-03-01
description <b> </b>Understanding sentiment changes in tourist flow is critical in designing exciting experiences for tourists and promoting sustainable tourism development. This paper proposes a novel analytical framework to investigate the tourist sentiment changes between different attractions based on geotagged social media data. Our framework mainly focuses on visualizing the detailed sentiment changes of tourists and exploring the valuable spatiotemporal pattern of the sentiment changes in tourist flow. The tourists were first identified from social media users. Then, we accurately evaluated the tourist sentiment by constructing a Chinese sentiment dictionary, grammatical rule, and sentiment score. Based on the location information of social media data, we built and visualized the tourist flow network. Last, to further reveal the impact of attractions on the sentiment of tourist flow, the positive and negative sentiment profiles were generated by mining social media texts. We took Beijing, a famous tourist destination in China, as a case study. Our results revealed the following: (1) the temporal trend of tourist sentiment has seasonal characteristics and is significantly influenced by government control policies against COVID-19; (2) due to the impact of the attraction’s historical background, some tourist flows with highly decreased sentiment strength are linked to attractions; (3) on the long journey to the attraction, the sentiment strength of tourists decreases; and (4) bad traffic conditions can significantly decrease tourist sentiment. This study highlights the methodological implications of visualizing sentiment changes during collective tourist movement and provides comprehensive insight into the spatiotemporal pattern of tourist sentiment.
topic tourist flow
sentiment change
spatiotemporal analysis
geotagged social media data
url https://www.mdpi.com/2220-9964/10/3/135
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