Mining user viewpoints in online discussions

• Main Author: QIU, Minghui
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2015
• Subjects: user viewpoint; topic model; sentiment analysis; opinion mining; social media; Computer Sciences; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/etd_coll/127; https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=1123&context=etd_coll
• Institution: Singapore Management University

Online discussion forums are a type of social media which contains rich usercontributed facts, opinions, and user interactions on diverse topics. The large volume of opinionated data generated in online discussions provides an ideal testbed for user opinion mining. In particular, mining user opinions on social and political issues from online discussions is useful not only to government organizations and companies but also to social and political scientists. In this dissertation, we propose to study the task of mining user viewpoints or stances from online discussions on social and political issues. Specifically, we will talk about our proposed approaches for these sub-tasks, namely, viewpoint discovery, micro-level and macro-level stance prediction, and user viewpoint summarization. We first study how to model user posting behaviors for viewpoint discovery. We have two models for modeling user posting behaviors. Our first model takes three important characteristics of online discussions into consideration: user consistency, topic preference, and user interactions. Our second model focuses on mining interaction features from structured debate posts, and studies how to incorporate such features for viewpoint discovery. Second, we study how to model user opinions for viewpoint discovery. To model user opinions, we leverage the advances in sentiment analysis to extract users opinions in their arguments. Nevertheless, user opinions are sparse in social media and therefore we propose to apply collaborative filtering through matrix factorization to generalize the extracted opinions. Furthermore, we study micro-level and macro-level stance prediction. We propose an integrated model that jointly models arguments, stances, and attributes. Last but not least, we seek to summarize the viewpoints by finding representative posts as one may find the amount of posts holding the same viewpoint is still large. In summary, this dissertation discusses a number of key problems in mining user viewpoints in online discussions and proposes appropriate solutions to these problems. We also discuss other related tasks and point out some future work.