Model-based feedback in the language modeling approach to information retrieval
Proceedings of the tenth international conference on Information and knowledge management
A study of smoothing methods for language models applied to information retrieval
ACM Transactions on Information Systems (TOIS)
A cross-collection mixture model for comparative text mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Determining the semantic orientation of terms through gloss classification
Proceedings of the 14th ACM international conference on Information and knowledge management
Topic sentiment mixture: modeling facets and opinions in weblogs
Proceedings of the 16th international conference on World Wide Web
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Retrieval and feedback models for blog feed search
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
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
Bloggers as experts: feed distillation using expert retrieval models
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Key blog distillation: ranking aggregates
Proceedings of the 17th ACM conference on Information and knowledge management
An effective statistical approach to blog post opinion retrieval
Proceedings of the 17th ACM conference on Information and knowledge management
Adaptive subjective triggers for opinionated document retrieval
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Integrating Proximity to Subjective Sentences for Blog Opinion Retrieval
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Improving Opinion Retrieval Based on Query-Specific Sentiment Lexicon
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Facet-based opinion retrieval from blogs
Information Processing and Management: an International Journal
A unified relevance model for opinion retrieval
Proceedings of the 18th ACM conference on Information and knowledge management
ContentEx: a framework for automatic content extraction programs
ISI'09 Proceedings of the 2009 IEEE international conference on Intelligence and security informatics
Proximity-based opinion retrieval
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
ACM SIGIR Forum
A probabilistic model for opinionated blog feed retrieval
Proceedings of the 20th international conference companion on World wide web
Blog opinion retrieval based on topic-opinion mixture model
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
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This paper aims to find blog feeds having a principal inclination towards making opinionated comments on the given topic, so that we can subscribe to them to track influential and interesting opinions in the blogosphere. One major challenge is assigning topic-related opinion scores to blog feeds, which is embodied in two aspects. Firstly, we should identify whether the blog feed has a principal on-topic opinionated inclination. This inclination should be collectively revealed by all posts of the feed. We should fully consider evidences from all the posts of the feed to identify salient information among many posts of the feed. Secondly, we should capture topic-related opinions in the blog feed while ignoring irrelevant opinions. In this paper, we propose a unified framework for opinionated blog feed retrieval, which combines topic relevance and opinion scores with a generative model. Furthermore, we propose a language modeling approach to estimating opinion scores that is seamlessly integrated into the framework, where two language models, Topic-specific Opinion Model (TOM) and Topic-biased Feed Model (TFM), work collectively to reflect whether the blog feed shows a principal on-topic opinionated inclination. To estimate TFM, we propose a topic-biased random walk to exploit both content and structural information to capture topic-biased salient information in the feed. As for TOM estimation, we propose to use a generative mixture model with prior guidance to effectively capture topic-specific opinion expressing language usage. The conducted experiments in the context of the TREC 2009-2010 Blog Track show the effectiveness of our proposed approaches.