Summarizing and extracting online public opinion from blog search results

Abstract

As more and more people are willing to publish their attitudes and feelings in blogs, how to provide an efficient way to summarize and extract public opinion in blogosphere has become a major concern for both compute science researchers and sociologist. Different from existing literatures on opinion retrieval and summarization, the major issue of online public opinion monitoring is to find out people's typical opinions and their corresponding distributions on the Web. We observe that blog search results could provide a very useful source for topic-coherent and authoritative opinions of the given query word. In this paper, a lexicon based method is proposed to enrich the representation of blog search results and a spectral clustering algorithm is introduced to partition blog search results into opinion groups, which help us to find out opinion distributions on the Web. A mutual reinforcement random walk model is proposed to rank result items and extract key sentiment words simultaneously, which facilitates user to quickly get the typical opinions of a given topic. Extensive experiments with different query words were conducted based on a real world blog search engine and the experiments results verify the efficiency and effectiveness of our proposed model and methods. © Springer-Verlag Berlin Heidelberg 2010.