WIDIT in TREC 2007 Blog Track: Combining Lexicon-Based Methods to Detect Opinionated Blogs

In TREC-2007, Indiana University‟s WIDIT Lab 1 participated in the Blog track‟s opinion task and the polarity subtask. For the opinion task, whose goal is to "uncover the public sentiment towards a given entity/target", we focused on combining multiple sources of evidence to detect opinionated blog postings. Since detecting opinionated blogs on a given topic (i.e., entity/target) involves not only retrieving topically relevant blogs but also identifying those that contain opinions about the target, our approach to the opinion finding task consisted of first applying traditional IR methods to retrieve on-topic blogs and then boosting the ranks of opinionated blogs based on combined opinion scores generated by multiple opinion detection methods. The key idea underlying our opinion detection method is to rely on a variety of complementary evidences rather than trying to optimize a single approach. This fusion approach to opinionated blog detection is motivated by our past experience that suggested no single approach, whether lexicon-based or classifier-driven, is well-suited for the blog opinion retrieval task. To accomplish the polarity subtask, which requires classification of the retrieved blogs into positive or negative orientation, our opinion detection module was extended to generate polarity scores to be used for polarity determination.