新冠疫情背景下公众出行情感的时空分异特征——以微博文本为例

Analysis of Spatiotemporal Differentiation Characteristics of Public Travel Emotions under the Background of the COVID-19 Pandemic—Taking Weibo Text as an Example

摘要: 采集我国2020年1月23日~2021年1月23日在COVID-19疫情期间的微博数据，采用Snow NLP情感分析模型、ROST CM6社会语义网络分析方法、地理空间分析等方法，通过计算公众出行情感倾向值，探讨COVID-19疫情背景下公众出行关注主题的变化以及公众出行情感的时空分异特征。结果表明：疫情各阶段的语义分析网络呈现由密集→稀疏→稀疏→密集的变化特征，公众出行关注的主题变化明显，其中出行目的地的变化最明显；新冠疫情变化与公众出行情感变化在时空尺度上紧密相关，其敏感地、较强地制约公众出行意愿；疫情作用下公众的出行情感呈现典型的时空分异特征，由于疫情的消极效应、滞后消极效应，随着疫情由暴发→好转→稳定好转→反复变化，公众出行情感由积极→消极→积极→消极变化，且这种演变特征在我国西南地区表现最为明显。

Abstract: Weibo text data during the period of January 23, 2020 to January 23, 2021 were collected; the value of the public’s emotional tendency towards travel and its spatio-temporal differentiation characteristics during the Pandemic period were analyzed utilizing the methods of sentiment analysis model, social semantic network analysis and geospatial analysis. The results indicated that the relationships between semantic networks showed a change characteristic from dense to sparse to sparse to dense, and the theme of public travel changed significantly. The change of COVID-19 Pandemic was closely related to the change of public’s emotional tendency towards travel; the COVID-19 Pandemic sensitively and strongly restricted the public’s willingness to travel. The public’s travel emotion presented typical characteristics of spatio-temporal differentiation under the influence of COVID-19 Pandemic. Due to the negative effects and lagging negative effects of the COVID-19 Pandemic, the public’s travel emotion changed from positive to negative to positive to negative as the COVID-19 Pandemic changed from outbreak to improvement to stable improvement to repeated changes, and the changing characteristic was most obvious in Southwest China.