  Shared travel has gradually become one of the hot topics discussed on social
networking platforms such as Micro Blog. In a timely manner, deeper network
community detection on the evaluation content of shared travel in social
networks can effectively conduct research and analysis on the public opinion
orientation related to shared travel, which has great application prospects.
The existing community detection algorithms generally measure the similarity of
nodes in the network from the perspective of spatial distance. This paper
proposes a Community detection algorithm based on Textual content Similarity
and sentimental Tendency (CTST), considering the network structure and node
attributes at the same time. The content similarity and sentimental tendency of
network community users are taken as node attributes, and on this basis, an
undirected weighted network is constructed for community detection. This paper
conducts experiments with actual data and analyzes the experimental results. It
is found that the modularity of the community detection results is high and the
effect is good.
