Foundations of statistical natural language processing
Foundations of statistical natural language processing
Relevance based language models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Selection criteria for word trigger pairs in language modelling
ICG! '96 Proceedings of the 3rd International Colloquium on Grammatical Inference: Learning Syntax from Sentences
Combining the language model and inference network approaches to retrieval
Information Processing and Management: an International Journal - Special issue: Bayesian networks and information retrieval
An application of text categorization methods to gene ontology annotation
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Tracking Information Epidemics in Blogspace
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Flexible pseudo-relevance feedback via selective sampling
ACM Transactions on Asian Language Information Processing (TALIP)
Regularized estimation of mixture models for robust pseudo-relevance feedback
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
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
Identifying the influential bloggers in a community
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
A holistic lexicon-based approach to opinion mining
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
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
Limits of opinion-finding baseline systems
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
ACM SIGIR Forum
Opinion finding in blogs: a passage-based language modeling approach
RIAO '10 Adaptivity, Personalization and Fusion of Heterogeneous Information
Find me opinion sources in blogosphere: a unified framework for opinionated blog feed retrieval
Proceedings of the fifth ACM international conference on Web search and data mining
Information Retrieval on the Blogosphere
Foundations and Trends in Information Retrieval
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This paper proposes a novel application of a statistical language model to opinionated document retrieval targeting weblogs (blogs). In particular, we explore the use of the trigger model---originally developed for incorporating distant word dependencies---in order to model the characteristics of personal opinions that cannot be properly modeled by standard n-grams. Our primary assumption is that there are two constituents to form a subjective opinion. One is the subject of the opinion or the object that the opinion is about, and the other is a subjective expression; the former is regarded as a triggering word and the latter as a triggered word. We automatically identify those subjective trigger patterns to build a language model from a corpus of product customer reviews. Experimental results on the TREC Blog Track test collections show that, when used for reranking initial search results, our proposed model significantly improves opinionated document retrieval by over 20% in MAP. In addition, we report on an experiment on dynamic adaptation of the model to a given query, which is found effective for most of difficult queries categorized under politics and organizations.