Mining contrastive opinions on political texts using cross-perspective topic model

This paper presents a novel opinion mining research problem, which is called Contrastive Opinion Modeling (COM). Given any query topic and a set of text collections from multiple perspectives, the task of COM is to present the opinions of the individual perspectives on the topic, and furthermore to quantify their difference. This general problem subsumes many interesting applications, including opinion summarization and forecasting, government intelligence and cross-cultural studies. We propose a novel unsupervised topic model for contrastive opinion modeling. It simulates the generative process of how opinion words occur in the documents of different collections. The ad hoc opinion search process can be efficiently accomplished based on the learned parameters in the model. The difference of perspectives can be quantified in a principled way by the Jensen-Shannon divergence among the individual topic-opinion distributions. An extensive set of experiments have been conducted to eva...

• Type: Journal
• Year: 2012
• Where: WSDM
• Authors: Yi Fang, Luo Si, Naveen Somasundaram, Zhengtao Yu
• Keywords: Data Mining, Information Storage And Retrieval, Natural Language Processing, Representative Media, WSDM 2012