Abstract
In recent years, opinion retrieval attracted a growing research interest as online users' opinions become more and more valuable for market survey, political polls, etc. The goal of opinion retrieval is to find relevant and opinionate documents according to a user's query. Compared with previous lexicon-based generative model for opinion retrieval considering that the sentiment words are equal for a query, which cannot reflect different sentiment words' relevant opinion strength, we propose a graph-based approach by using HITS model to capture the sentiment words' relevant opinion strength. Then the weights are incorporated into the weighted lexicon-based generative model for opinion retrieval. Experimental results on two datasets show the effectiveness of the proposed generative model. Compared with the baseline approach, improvements of 4% and 11% have been obtained on two real datasets.
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