论文标题
基于文本内容相似性和感性趋势的社交网络社区检测
Social Network Community Detection Based on Textual Content Similarity and Sentimental Tendency
论文作者
论文摘要
共享旅行逐渐成为社交网络平台上讨论的热门话题之一，例如Micro Blog。及时，对社交网络共享旅行的评估内容的更深层次的网络社区检测可以有效地对与共享旅行相关的公众舆论取向进行研究和分析，而共享旅行具有很高的应用程序前景。现有的社区检测算法通常从空间距离的角度来测量网络中节点的相似性。本文提出了一种基于文本内容的相似性和情感趋势（CTST）的社区检测算法，同时考虑了网络结构和节点属性。网络社区用户的内容相似性和情感趋势被视为节点属性，在此基础上，构建了无方向的加权网络供社区检测。本文通过实际数据进行实验，并分析实验结果。发现社区检测结果的模块化很高，效果很好。
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.
