[Apologies if you receive this more than once] #################################################################### International Workshop on Topic Feature Discovery and Opinion Mining (TFDOM'10) CALL FOR PAPERS #################################################################### International Workshop on Topic Feature Discovery and Opinion Mining (TFDOM'10) Joint with the 10th IEEE International Conference on Data Mining (ICDM'10) December 14, 2010, Sydney, Australia Homepage: http://www.tfdom-workshop.org/ #################################################################### # Full paper submission deadline: *** July 23, 2010 *** # Notification of acceptance: September 20, 2010 # Camera-ready of accepted papers: October 11, 2010 # Workshop: December 13, 2010 #################################################################### Textual data in the world can be roughly categorized into two main types: facts and opinions. Much effort has been devoted to fact- based information processing in the past decades, and many useful techniques have been developed for information retrieval or text mining. In recent years opinion-based information processing has also been receiving increasingly more attention from researchers. Understanding people's opinions about some subject matters or issues is important for organizational decision making in general. For instance, organizations are keen on retrieving and analyzing customers' opinions about products and services so as to develop more effective business strategies for product design and customer centric marketing. Nevertheless, identifying opinion sources, extracting prominent topic features, summarizing relevant opinions, and effectively predicting the polarity of an opinion are all very challenging tasks. These open research problems are the primary focuses of this Topic Feature Discovery and Opinion Mining Workshop. Topic feature discovery aims to identify on-topic information sources and extract relevant features for a given topic (e.g., a person, an event, or a government policy). The results of many empirical experiments suggested that the effectiveness of traditional text mining methods might be hindered when they were applied to topic feature discovery from opinionated sources. This might be caused by the nature of different problems being tackled, and/or by the inappropriate effectiveness measures borrowed from classical data mining research. For instance, the widely used measures such as support and confidence, turn out to be unsuitable for the leveraging stage. By way of illustration, given a specified topic, usually a highly frequent pattern (normally short in length) is general in semantics and a specific pattern is long in length and low in frequency. The objective of research on topic feature discovery is to design and develop effective and efficient methods to extract subset of features from textual document to describe the specific topics or opinion holders. Opinion mining, also known as sentiment analysis, aims to summarize and classify opinionated expressions. When compared with traditional fact-based text analysis, research on opinion mining tries to address the new problems related to the identification and analysis of opinions about some topics or facts. More specifically, opinion mining techniques have been applied to predicting the polarity (or inclination) of an opinionated expression related to a topic (i.e., an opinion holder). They have also been applied to consolidating and summarizing the possibly contradictory opinions from a large number of electronic documents such as blogs, online news, consumer comments that contain opinionated expressions. The fundamental problems in opinion mining research include the retrieval of opinionated expressions, identification of opinion holders or the specific features of the opinion holders, classification of the polarities of sentiments related to some opinion holders, fine-grained analysis of feature-based sentiments, detection of opinion spam, and application of opinion analysis to real-world problem solving or decision making. As a matter of fact, there are many opportunities and challenges for extensive research in the field of opinion mining. Being inter-related, topic feature discovery and opinion mining are highly challenging topics in modern information analysis, from both an empirical and a theoretical perspective. They are also the important issues and the critical steps for Web personalization applications and recommender systems. The research problems related to these two topics have attracted increasingly more attention from researchers in the communities of data mining, Web intelligence, text mining, machine learning, natural language processing, and information retrieval. By highly focusing on these two challenging research topics and their related areas, this workshop aims to advance the theories and techniques for text mining in general and opinion mining in particular, and to explore novel methodologies for the discovery and interpretation of useful and interesting knowledge embedded in textual documents. ++++++++++++++++++++++++++ TOPIC OF INTERESTS ++++++++++++++++++++++++++ Topics include, but are not limited to: - Relevant feature discovery - Opinion mining and sentiment analysis - Multilingual opinion summarization - Sentiment and subjectivity classification - Feature-based sentiment analysis - Information filtering and retrieval - Text mining - Text categorizations - Ontology mining and ontology merging - Information extraction - Recommender systems - Web personalization and opinion analysis - Evaluation methodologies for topic feature discovery and opinion mining - Industrial applications of topic feature discovery and sentiment analysis +++++++++++++++++ KEYNOTE SPEAKER +++++++++++++++++ Prof. Bing Liu from University of Illinois at Chicago, USA, will deliver a talk in the workshop. Prof. Liu is a well-known researcher highly active in the fields of text mining and opinion mining. He has been participating in these areas for more than ten years. The methods developed and the scientific findings discovered by him and his team have made significant impacts on the data mining, text mining, and opinion mining communities. ++++++++++++++++++++++++++++++++++++ ONLINE SUBMISSIONS AND PUBLICATIONS ++++++++++++++++++++++++++++++++++++ Paper submissions should be limited to a maximum of 10 pages in the IEEE 2-column format http://www.computer.org/portal/web/cscps/formatting. All papers will be double-blind reviewed by the Program Committee on the basis of technical quality, relevance to data mining, originality, significance, and clarity. Papers that do not comply with the Submission Guidelines will be rejected without review. High quality papers in all data mining areas are solicited. Original papers exploring new directions will receive especially careful consideration. Papers that have already been accepted or are currently under review for other conferences or journals will not be considered for publication. Accepted papers will be published in the conference proceedings by the IEEE Computer Society Press and accorded oral presentation times in the main conference. Submissions accepted will be allocated 10 pages in the proceedings. ++++++++++++++++++ IMPOTANT DATES ++++++++++++++++++ Full paper submission deadline: *** July 23, 2010 *** Notification of acceptance: September 20, 2010 Camera-ready of accepted papers: October 11, 2010 Workshop: December 13, 2010 ++++++++++++++++++++++++ WORKSHOP ORGANIZATION ++++++++++++++++++++++++ Program Committee * Albert Au yeung, NTT Communication Science Laboratories, Japan * Ling Chen, University of Technology, Sydney, Australia * Michael Gamon, Microsoft Research, USA * Xiaoying Gao, Victoria University of Wellington, New Zealand * Shlomo Geva, Queensland University of Technology, Australia * Jimmy Huang, Youk University, Canada * Qingliang Miao, Chinese Academy of Sciences, China * Stuart E. Middleton, University of Southampton, UK * Chunping Li, Qinghua University, China * Qiudan Li, Chinese Academy of Sciences, China * Tao Li, Florida International University, USA * Wenjie Li, Hong Kong Polytechnic University, China * Yang Liu, York University, Canada * Yue Lu, University of Illinois at Urbana-Champaign, USA * Manabu Okumura, Tokyo Institute of Technology, Japan * Luiz Augusto Pizzato, University of Sydney, Australia * Franco Salvetti, Microsoft Corp., USA * Giovanni Semeraro, University of Bari, Italy * Dian Tjondronegoro, Queensland University of Technology, Australia * Hui Wang, Ulster University, UK * Ozlem Uzuner, Massachusetts Institute of Technology, USA * Yue Xu, Queensland University of Technology, Australia * Yiyu Yao, Regina University, Canada * Bei Yu, Syracuse University, USA * Yunqing Xia, Qinghua University, China * Wei Xu, Renmin Univeristy, China * Markus Zanker, University Klagenfurt, Austria * Daniel Zeng. The University of Arizona, USA * Songmao Zhang, Chinese Academy of Sciences, China * Yanchang Zhao, Centrelink, Australia Workshop Co-Chairs * Yuefeng Li Queensland University of Technology, Australia Email: y2.li@qut.edu.au * Ning Zhong Maebashi Institute of Technology, Japan Email: zhong@maebashi-it.ac.jp * Raymond Y. K. Lau City University of Hong Kong, Hong Kong Email: raylau@cityu.edu.hk Workshop Publicity Chair * Xiaohui (Daniel) Tao Queensland University of Technology, Australia ******* Contact Information ******** Email: Yuefeng Li (y2.li@qut.edu.au) Xiaohui (Daniel) Tao (x.tao@qut.edu.au)