Passage-level evidence in document retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
A general language model for information retrieval (poster abstract)
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Passage retrieval based on language models
Proceedings of the eleventh international conference on Information and knowledge management
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Adaptive subjective triggers for opinionated document retrieval
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Investigating Learning Approaches for Blog Post Opinion Retrieval
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Challenges for Sentence Level Opinion Detection in Blogs
ICIS '09 Proceedings of the 2009 Eigth IEEE/ACIS International Conference on Computer and Information Science
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In this work, we propose a Passage-Based Language Modeling (LM) approach for Opinion Finding in Blogs. Our decision to use Language Modeling in this work is totally based on the importance of passages in blogposts and performance LM has given in various Opinion Detection approaches. In addition to this, we propose a novel method for bi-dimensional Query Expansion with relevant and opinionated terms using Wikipedia and Relevance-Feedback mechanism respectively. Besides all this, we also compare the performance of three Passage-based document ranking functions (Linear, Avg, Max). For evaluation purposes, we use the data collection of TREC Blog06 with 50 topics of TREC 2006 over TREC provided best baseline with opinion finding MAP of 0.3022. Our approach gives a MAP improvement of almost 9.29% over best TREC provided baseline (baseline4).