Complementary Aspect-Based Opinion Mining

• Published in: IEEE transactions on knowledge and data engineering Vol. 30; no. 2; pp. 249 - 262
• Main Authors: Zuo, Yuan, Wu, Junjie, Zhang, Hui, Wang, Deqing, Xu, Ke
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Analytical models; Aspect-based opinion mining; Collections; Data mining; Dirichlet problem; dirichlet process; Entropy; Labeling; Labelling; latent dirichlet allocation (LDA); maximum entropy model; Media; Sentiment analysis; Social network services; word embedding
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2017.2764084

Aspect-based opinion mining is finding elaborate opinions towards a subject such as a product or an event. With explosive growth of opinionated texts on the Web, mining aspect-level opinions has become a promising means for online public opinion analysis. In particular, the boom of various types of online media provides diverse yet complementary information, bringing unprecedented opportunities for cross media aspect-opinion mining. Along this line, we propose CAMEL, a novel topic model for complementary aspect-based opinion mining across asymmetric collections. CAMEL gains information complementarity by modeling both common and specific aspects across collections, while keeping all the corresponding opinions for contrastive study. An auto-labeling scheme called AME is also proposed to help discriminate between aspect and opinion words without elaborative human labeling, which is further enhanced by adding word embedding-based similarity as a new feature. Moreover, CAMEL-DP, a nonparametric alternative to CAMEL is also proposed based on coupled Dirichlet Processes. Extensive experiments on real-world multi-collection reviews data demonstrate the superiority of our methods to competitive baselines. This is particularly true when the information shared by different collections becomes seriously fragmented. Finally, a case study on the public event "2014 Shanghai Stampede" demonstrates the practical value of CAMEL for real-world applications.