Enhancing Low Carbon Awareness in Social Media Discourse: A Fuzzy Clustering Approach

Abstract

The frequent occurrence of extreme weather makes people pay more attention to environmental protection. To cope with the global climate problem, various countries re-plan social development through the concept of low-carbon. As greatly popularized by the Internet, the topic of low carbon concept is spread more through online social media, so it is urgent to understand the user’s attention to low carbon topics in a more intelligent way for subsequent relevant publicity and policy guidance. This paper studies the low-carbon topic of attention in the context of social media. First, the BERT (Bidirectional Encoder Representation from Transformers) model is used to complete the word vector feature extraction of acquired data; Secondly, the FCM method was used to complete the clustering analysis of the main topics in the low-carbon concept, and the PSO method was used to optimize the model. After optimization, the accuracy of clustering for various topics was higher than 80%. For the Esse index of cluster center variance, the method proposed in this article is also close to 10% due to other classic methods; Finally, this paper carried out an application test of low-carbon topics in the region, achieved good results, and made a detailed analysis of the distribution of various topics. It can be predicted that this method will provide more public opinion references for low-carbon development paths in various countries and regions in the future, and provide technical support for information dissemination and analysis under social media.

Keywords

• Social media
• FCM
• low carbon
• text clustering