基于复杂网络融合产品主题的重要在线评论挖掘研究.

Mining important reviews from the vast amounts of online reviews is the key to help consumers making quick decision. Based on complex network theory, this paper constcucted online reviews network through regarding reviews' content as the network nodes and the semantic similarity between reviews as the weights of link. It demonstrated the rationality of the network through the analysis of the global statistics of reviews network. And it divided the reviews network community of subject-oriented according to the community structure features of reviews network. Based on PageRank algorithms, it built a multiple-attribute decision-making method of important reviews in combination with the node importance evaluation methods of complex network and community attribute. Simulation experiments verify the feasibility and accuracy of the method in the global and local network. [ABSTRACT FROM AUTHOR]