a generation model to unify topic relevance and lexicon-based sentiment for opinion retrieval
Opinion retrieval is a task of growing interest in social life and academic research, which is to find relevant and opinionate documents according to a user's query. One of the key issues is how to combine a document's opinionate score (the ranking score of to what extent it is subjective or objective) and topic relevance score. Current solutions to document ranking in opinion retrieval are generally ad-hoc linear combination, which is short of theoretical foundation and careful analysis. In this paper, we focus on lexicon-based opinion retrieval. A novel generation model that unifies topic-relevance and opinion generation by a quadratic combination is proposed in this paper. With this model, the relevance-based ranking serves as the weighting factor of the lexicon-based sentiment ranking function, which is essentially different from the popular heuristic linear combination approaches. The effect of different sentiment dictionaries is also discussed. Experimental results on TREC blog datasets show the significant effectiveness of the proposed unified model. Improvements of 28.1\% and 40.3\% have been obtained in terms of MAP and p@10 respectively. The conclusion is not limited to blog environment. Besides the unified generation model, another contribution is that our work demonstrates that in the opinion retrieval task, a Bayesian approach to combining multiple ranking functions is superior to using a linear combination. It is also applicable to other result re-ranking applications in similar scenario.
generation model topic relevance opinion retrieval opinion generation model sentiment analysis