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Shared travel has gradually become one of the hot topics discussed on socialnetworking platforms such as Micro Blog. In a timely manner, deeper networkcommunity detection on the evaluation content of shared travel in socialnetworks can effectively conduct research and analysis on the public opinionorientation related to shared travel, which has great application prospects.The existing community detection algorithms generally measure the similarity ofnodes in the network from the perspective of spatial distance. This paperproposes a Community detection algorithm based on Textual content Similarityand sentimental Tendency (CTST), considering the network structure and nodeattributes at the same time. The content similarity and sentimental tendency ofnetwork community users are taken as node attributes, and on this basis, anundirected weighted network is constructed for community detection. This paperconducts experiments with actual data and analyzes the experimental results. Itis found that the modularity of the community detection results is high and theeffect is good.

Social Network Community Detection Based on Textual Content Similarity and Sentimental Tendency