Sui Feixue, Zhang Hengxu
Capital University of Economics and Business, College of Business Administration, Beijing, China.
Shandong Agricultural University, College of Economics and Management (Business College), Taian, China.
Heliyon. 2024 Sep 27;10(19):e38510. doi: 10.1016/j.heliyon.2024.e38510. eCollection 2024 Oct 15.
The attention and sentiment of the public are crucial for better implementation of waste sorting behaviors and moving towards green living. In this study, web scraping technology was used to collect 367,856 Weibo posts related to waste sorting from Sina Weibo. Utilizing text co-occurrence networks, Latent Dirichlet Allocation (LDA) topic modeling, and a deep learning model combining the Affective Cognitive Model (OCC) with Long Short-Term Memory Model (LSTM) (referred to as OCC-LSTM), we comprehensively understand the text at both micro and macro levels, analyzing the attention and sentiment of the public towards waste sorting behaviors on the Sina Weibo platform. Several important findings emerged from the empirical results. First, highly engaging posts were predominantly published by users with a large following, and the number of posts fluctuated over time. This reflects the influence of social hot topics and the timeliness of information dissemination. Second, there was heterogeneity in the user groups and their locations, often influenced by cultural differences due to geographical location. Third, positive sentiment towards waste sorting behavior was higher than negative sentiment on the Weibo platform. Moreover, public attention varied under different emotional influences concerning the topic of waste sorting behavior. The innovation of this study lies in the development of a research framework combining co-occurrence networks and deep learning, expanding the analysis on both micro and macro levels. This framework broadens the research paradigms and dimensions of public perception in waste sorting. This study is significant for promoting waste sorting behaviors and implementing climate policies.
公众的关注和情绪对于更好地实施垃圾分类行为以及迈向绿色生活至关重要。在本研究中，利用网络爬虫技术从新浪微博收集了367,856条与垃圾分类相关的微博帖子。通过文本共现网络、潜在狄利克雷分配（LDA）主题建模以及将情感认知模型（OCC）与长短期记忆模型（LSTM）相结合的深度学习模型（称为OCC-LSTM），我们在微观和宏观层面全面理解文本，分析新浪微博平台上公众对垃圾分类行为的关注和情绪。实证结果得出了几个重要发现。首先，高参与度的帖子主要由拥有大量粉丝的用户发布，且帖子数量随时间波动。这反映了社会热点话题的影响以及信息传播的及时性。其次，用户群体及其所在位置存在异质性，这通常受地理位置造成的文化差异影响。第三，微博平台上对垃圾分类行为的积极情绪高于消极情绪。此外，公众对垃圾分类行为主题的关注在不同情感影响下有所不同。本研究的创新之处在于开发了一个将共现网络与深度学习相结合的研究框架，在微观和宏观层面都进行了拓展分析。该框架拓宽了公众对垃圾分类认知的研究范式和维度。本研究对于促进垃圾分类行为和实施气候政策具有重要意义。
