Learning to cluster web search results
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Structure and evolution of blogspace
Communications of the ACM - The Blogosphere
A personalized search engine based on web-snippet hierarchical clustering
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Exploring in the weblog space by detecting informative and affective articles
Proceedings of the 16th international conference on World Wide Web
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
A generation model to unify topic relevance and lexicon-based sentiment for opinion retrieval
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Sentiment Clustering: A Novel Method to Explore in the Blogosphere
APWeb/WAIM '09 Proceedings of the Joint International Conferences on Advances in Data and Web Management
Improving similarity measures for short segments of text
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Answering opinion questions with random walks on graphs
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Similarity measures for short segments of text
ECIR'07 Proceedings of the 29th European conference on IR research
Getting emotional about news summarization
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
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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.