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Mining public sentiments and perspectives from geotagged social media data for appraising the post-earthquake recovery of tourism destinations
Applied Geography ( IF 4.732 ) Pub Date : 2020-10-01 , DOI: 10.1016/j.apgeog.2020.102306
Yingwei Yan , Jingfu Chen , Zhiyong Wang

Abstract Post-disaster recovery involves interdependent processes of physical and psychological rehabilitations. Over the past few years, researchers have explored geotagged social media data to assist the planning, monitoring, and assessment of the post-disaster recovery of tourism destinations, given its advantages over traditional approaches. Nonetheless, recent studies have mostly focused on quantitatively accessing the physical elements of post-disaster recovery (e.g., infrastructure reconstruction and re-influx of tourists). Few studies have explored people's sentiments and perspectives over the process of post-disaster recovery. In this study, a mixed methods approach involving sentiment analysis and Latent Dirichlet allocation (LDA) topic modeling is designed for mining sheer volume of tweets about Lombok and Bali, generated by nonlocal Twitter users after a series of earthquakes in the two places in August 2018. The findings mainly suggest that people have generally become less negative about Lombok and Bali over time, despite fluctuations in their sentiment polarities' central tendencies. In addition, dissatisfactions about the housing reconstruction progress, tourism recovery status, and living conditions in the affected areas of Lombok still existed in 2019; contestations have been found with regard to the huge funds for hosting the 2018 Bali IMF-World Bank meeting after the earthquakes. The overall results of this study have proved that the adopted approach can effectively reveal the variations of people's sentiments and perspectives of general and specific issues regarding post-disaster tourism recovery over time.

中文翻译:

从地理标记的社交媒体数据中挖掘公众情绪和观点以评估旅游目的地的震后恢复

摘要 灾后恢复涉及相互依赖的身心康复过程。在过去的几年里,研究人员探索了地理标记的社交媒体数据,以帮助规划、监测和评估旅游目的地的灾后恢复,因为它优于传统方法。尽管如此,最近的研究主要集中在定量获取灾后恢复的物理要素(例如,基础设施重建和游客重新涌入)。很少有研究探讨人们对灾后恢复过程的情绪和观点。在这项研究中,设计了一种涉及情感分析和潜在狄利克雷分配 (LDA) 主题建模的混合方法,用于挖掘关于龙目岛和巴厘岛的大量推文,由非本地 Twitter 用户在 2018 年 8 月两地发生一系列地震后生成。 调查结果主要表明,尽管人们的情绪极性的中心倾向发生了波动,但随着时间的推移,人们对龙目岛和巴厘岛的负面情绪普遍减少。此外,2019年龙目岛受影响地区的房屋重建进度、旅游恢复状况、生活条件等方面的不满情绪依然存在;关于在地震后举办 2018 年巴厘岛国际货币基金组织 - 世界银行会议的巨额资金,已经存在争议。本研究的总体结果证明,所采用的方法可以有效地揭示人们对灾后旅游恢复的一般和具体问题的情绪和观点随时间的变化。
更新日期:2020-10-01
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