A weighted lexicon-based generative model for opinion retrieval

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.