[Elsnet-list] CfP: Workshop on Topic Feature Discovery and Opinion Mining (TFDOM'10)@ICDM'10

Daniel Tao x.tao at qut.edu.au
Thu Jun 3 08:30:32 CEST 2010


 [Apologies if you receive this more than once]

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International Workshop on Topic Feature Discovery and Opinion Mining 
		       (TFDOM'10)

                   CALL FOR PAPERS
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International Workshop on Topic Feature Discovery and Opinion Mining 
(TFDOM'10) Joint with the 10th IEEE International Conference on Data 
Mining (ICDM'10)

December 13, 2010, Sydney, Australia

Homepage: http://sky.scitech.qut.edu.au/~li3/home.htm

####################################################################
# 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

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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 (more to be confirmed)

* 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
* Luiz Augusto Pizzato,  University of Sydney, Australia 
* 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 at qut.edu.au

* Ning Zhong
  Maebashi Institute of Technology, Japan
  Email: zhong at maebashi-it.ac.jp

* Raymond Y. K. Lau
  City University of Hong Kong, Hong Kong
  Email: raylau at cityu.edu.hk

Workshop Publicity Chair

* Xiaohui (Daniel) Tao
  Queensland University of Technology, Australia

******* Contact Information ********

Email: 	Yuefeng Li (y2.li at qut.edu.au)
	Xiaohui (Daniel) Tao (x.tao at qut.edu.au)


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