Exploiting social and local contexts propagation for inducing Chinese microblog-specific sentiment lexicons. (May 2019) - Zhao, Chuanjun; Wang, Suge; Li, Deyu
Abstract: Sentiment lexicons including opinion words, sentiment phrases, and idioms with sentiment polarities play an important role in sentiment analysis tasks. Apart from explicit sentiment features, extracting implicit sentiment features is a challenging research issue. The sentiment expression is very domain-specific, and constructing a general sentiment lexicon that is suitable for all domains is hard or even impossible. In this paper, we propose a novel sentiment unit context propagation framework to extract Chinese microblog-specific explicit and implicit sentiment features. In the process of the selection of seed sentiment units, we select the seed sentiment units that have a large standard degree of centrality with other units, and mark these units with sentiment labels using general sentiment lexicons and manual calibrations. To realize sentiment label propagation from a small amount of labeled sentiment units to unlabeled ones, we exploit local contexts, topic features, and so`cial relationships among users in microblog social networks. After that, the sentiment scores of units are calculated using unit context sentiment propagation. Experiments on two real-world microblog data sets demonstrate that our method can generate microblog-specific sentiment lexicons effectively. Furthermore, the sentiment classification accuracies significantly outperform state-of-the-art baselines.