On performance of Topical Opinion Retrieval

We investigate the effectiveness of both the standard evalua- tion measures and the opinion component for topical opinion retrieval. We analyze how relevance is affected by opinions by perturbing relevance ranking by the outcomes of opinion- only classifiers built by Monte Carlo sampling. Topical opin- ion rankings are obtained by either re-ranking or filtering the documents of a first-pass retrieval of topic relevance. The proposed approach establishes the correlation between the accuracy and the precision of the classifier and the perfor- mance of the topical opinion retrieval. Among other results, it is possible to assess the effectiveness of the opinion compo- nent by comparing the effectiveness of the relevance baseline with the topical opinion ranking.