Sentiment mining of commodity reputation data based on joint model
WANG Suge1,2, LI Dayu1, LI Yang1
1. School of Computer & Information Technology, Shanxi University, Taiyuan 030006, China;
2. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan 030006, China
Abstract：This paper presents a probabilistic graphical model to simultaneously extract objects, aspects and sentiments from commodity reputation data. The underlying assumption is that the aspect distribution depends on the object distribution, while the sentiment distribution depends on the aspect distribution. The model further assumes that words are the smallest sampling units and is fully unsupervised. Tests on car reputation data show that this model can predict the aspect and sentiment categories of commodity reviews and can simultaneously extract object information from the reviews.
王素格, 李大宇, 李旸. 基于联合模型的商品口碑数据情感挖掘[J]. 清华大学学报（自然科学版）, 2017, 57(9): 926-931.
WANG Suge, LI Dayu, LI Yang. Sentiment mining of commodity reputation data based on joint model. Journal of Tsinghua University(Science and Technology), 2017, 57(9): 926-931.
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2017, Vol. 57 
