Integrating Proximity to Subjective Sentences for Blog Opinion Retrieval

Opinion finding is a challenging retrieval task, where it has been shown that it is especially difficult to improve over a strongly performing topic-relevance baseline. In this paper, we propose a novel approach for opinion finding, which takes into account the proximity of query terms to subjective sentences in a document. We adapt two state-of-the-art opinion detection techniques to identify subjective sentences from the retrieved documents. Our first technique uses the OpinionFinder toolkit to classify the subjectiveness of sentences in a document. Our second technique uses an automatically generated dictionary of subjective terms derived from the document collection itself to identify the most subjective sentences in a document. We extend the Divergence From Randomness (DFR) proximity model to integrate the proximity of query terms to the subjective sentences identified by either of the proposed techniques. We evaluate these techniques on five different strong baselines across two different query datasets from the TREC Blog track. We show that we can significantly improve over the baselines and that, in several settings, our proposed techniques can at least match the top performing systems at the TREC Blog track.