Probabilistic opinion models based on subjective sources

Abstract

This article describes approaches for searching opinionated documents for a given query from a standard data collection. To detect if a text is opinionated (i.e., contain subjective information) or not, we propose two methods: the first method is based on lexicons of subjective words (i.e., SentiWordNet) supported by the assumption that more a document contains the subjective terms more it has the tendency of being an opinionated document while the second method is based on probabilistic model supporting the idea that given a document having a strong similarity with a reference opinionated text is more likely to be opinionated. In the second method, we take support of language modeling approach to compute this similarity. Experiments are conducted with TREC Blog06 as the test collection and the IMDB data collection as being the reference data collection. The experimental results report effectiveness of both methods.