Passage retrieval based on language models
Proceedings of the eleventh international conference on Information and knowledge management
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In this work, we evaluate the importance of Passages in blogs especially when we are dealing with the task of Opinion Detection. We argue that passages are basic building blocks of blogs. Therefore, we use Passage-Based Language Modeling approach as our approach for Opinion Finding in Blogs. Our decision to use Language Modeling (LM) in this work is totally based on the 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. We also compare the impacts of two different query terms weighting (and ranking) approaches on final results. 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).