Blog Opinion Retrieval with Generation Model and Mixture Model

Blog opinion retrieval aims to find blogs with opinionated information related to a given topic.Its main problem is to compute the opinion score,which balances topic relevance and opinion relevance.To deal with this problem a generative model deduced by a Bayesian approach is pro-posed,and an improved mixture model is proposed to estimate the opinion relevance between a blog and a given topic in our retrieval framework.Moreover,pointwise mutual information is used to expand sentiment words for different topics based on a general sentimental lexicon.The correlation between topic and candidate words is applied in the process of both expanding sentiment words and estimating sentence opinion scores.Experimental results show that the proposed approaches improve upon the state-of-the-art opinion retrieval method on TREC2010 dataset.

Learning from context： a mutual reinforcement model for Chinese microblog opinion retrieval

This study addresses the problem of Chinese microblog opinion retrieval, which aims to retrieve opinionated Chinese microblog posts relevant to a target specified by a user query. Existing studies have shown that lexicon-based approaches employed online public sentiment resources to rank sentiment words relying on the document features. However, this approach could not be effectively applied to mi- croblogs that have typical user-generated content with valu- able contextual information： ＂user-user＂ interpersonal interactions and ＂user-post/comment＂ intrapersonal interactions. This contextual information is very helpful in estimating the strength of sentiment words more accurately. In this study, we integrate the social contextual relationships among users, posts/comments, and sentiment words into a mutual reinforcement model and propose a unified three-layer heterogeneous graph, on which a random walk sentiment word weighting algorithm is presented to measure the strength of opinion of the sentiment words. Furthermore, the weights of sentiment words are incorporated into a lexicon-based model for Chinese microblog opinion retrieval. Comparative experiments are conducted on a Chinese microblog corpus, and the results show that our proposed mutual reinforcement model achieves significant improvement over previous methods.