Geospatial Semantics Analysis of the Qinghai–Tibetan Plateau Based on Microblog Short Texts

Abstract

Place descriptions record qualitative information related to places and their spatial relationships; thus, the geospatial semantics of a place can be extracted from place descriptions. In this study, geotagged microblog short texts recorded in 2017 from the Tibetan Autonomous Region and Qinghai Province were used to extract the place semantics of the Qinghai–Tibetan Plateau (QTP). ERNIE, a language representation model enhanced by knowledge, was employed to extract thematic topics from the microblog short texts, which were then geolocated and used to analyze the place semantics of the QTP. Considering the large number of microblogs published by tourists in both Qinghai and Tibet, we separated the texts into four datasets according to the user, i.e., local users in Tibet, tourists in Tibet, local users in Qinghai, and tourists in Qinghai, to explore the place semantics of the QTP from different perspectives. The results revealed clear spatial variability in the thematic topics. Tibet is characterized by travel- and scenery-related language, whereas Qinghai is characterized by emotion, work, and beauty salon-related language. The human cognition of place semantics differs between local residents and tourists, and with a greater difference between the two in Tibet than in Qinghai. Weibo texts also indicate that local residents and tourists are concerned with different aspects of the same thematic topics. The cities on the QTP can be classified into three groups according to their geospatial semantic components, i.e., tourism-focused, life-focused, and religion-focused cities.

Changes in Public Sentiment under the Background of Major Emergencies—Taking the Shanghai Epidemic as an Example

“…It has shown better performance than other models on Chinese language processing tasks [32]. Xu and Hu [33] applied ERNIE to study the Weibo comments of tourists in the Qinghai-Tibet Plateau and found that the cognition of local residents was focused on emotional expression. Zhang et al [34] combined the BiLSTM + attention + CRF model and ERNIE to study the public sentiment classification under the COVID-19 pandemic, solved the two tasks of sentiment dictionary expansion and sentiment classification, and further verified that ERNIE in Chinese text sentiment outperforms other models in classification.…”