Opinion finding in blogs: a passage-based language modeling approach

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).