Modeling Sentiment Evolution for Social Incidents
Yunjie Wang, Hui Li, Chen Lin · 2019
Modeling sentiment evolution for social incidents in Microblogs is of vital importance for both enterprises and government officials. Existing works on sentiment tracking are not satisfying, due to the lack of entity-level sentiment extraction and accurate sentiment shift detection. Identifying entity-level sentiment is challenging as Microbloggers often use multiple opinion expressions in a sentence which targets different entities. Moreover, the evolution of the background sentiment, which is essential to shift detection, is ignored in the previous study. To address these issues, we leverage the proximity information to obtain more precise entity-level sentiment extraction. Based on it, we propose to simultaneously model the evolution of background opinion and the sentiment shift using a state space model on the time series of sentiment polarities. Experiments on real data sets demonstrate that our proposed approaches outperform state-of-the-art methods on the task of modeling sentiment evolution for social incidents.