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WISDOM 2013 : Workshop on Issues of Sentiment Discovery and Opinion Mining


When Aug 11, 2013 - Aug 14, 2013
Where Chicago
Submission Deadline May 26, 2013
Notification Due Jun 8, 2013
Final Version Due Jun 18, 2013
Categories    NLP   sentiment analysis   opinion mining   web mining

Call For Papers

WISDOM (Workshop on Issues of Sentiment Discovery and Opinion Mining) aims to explore how the wisdom of the crowds is affecting (and will affect) the evolution of the Web and of businesses gravitating around it. In particular, the ACM KDD workshop series explores two different stages of sentiment analysis: the former focusing on the identification of opinionated text over the Web, the latter focusing on the classification of such text either in terms of polarity detection or emotion recognition.

The exponential growth of the Social Web is virally infecting more and more critical business processes such as customer support and satisfaction, brand and reputation management, product design and marketing. Because of this global trend, web users already evolved from the era of social relationships, in which they began to get connected and started to share contents, to the era of social functionality, in which they started using social networks as the main platform for communication and dissemination of information. Today, web users are going through the era of social colonization, in which every experience on the Web can be social (e.g., Facebook Like button), and are getting ready for the era of social context, in which web contents will be highly targeted and personalized. The final stage of such Social Web evolution is the so called era of social commerce, in which communities will define future products and services. In such context, the research field of sentiment analysis, which has already been rapidly growing in the last decade, is destined to become more and more important for Web and business dynamics.

The workshop aims to provide an international forum for both researchers and entrepreneurs working in the field of opinion mining to share information on their latest investigations in social information retrieval and their applications in academic research areas and industrial sectors. The broader context of the workshop comprehends AI, Semantic Web, information retrieval, web mining, and natural language processing. Topics of interest include but are not limited to:
• Sentiment identification & classification
• Knowledge-based opinion mining
• Concept-level opinion and sentiment analysis
• Sentiment summarization & visualization
• Semantic multi-dimensional scaling for sentiment analysis
• Entity discovery & extraction
• Opinion aggregation
• Opinion search & retrieval
• Domain adaptation for sentiment classification
• Time evolving sentiment analysis
• Opinion spam detection
• Comparative opinion analysis
• Topic detection & trend discovery
• Psychological models for sentiment analysis
• Biologically inspired opinion mining
• Affective knowledge acquisition for sentiment analysis
• Sentic computing
• Big social data analysis
• Social ranking
• Social network analysis
• Social media marketing
• Influence, trust & privacy analysis
• Business intelligence applications

• May 8th, 2013: Submission deadline
• June 8th, 2013: Notification of acceptance
• June 18th, 2013: Final manuscripts due
• August 12th, 2013: Workshop date

Accepted papers will be published in KDD WISDOM proceedings. Selected, expanded versions of papers presented at the workshop will be invited to a forthcoming Special Issue of Cognitive Computation on opinion mining and sentiment analysis.

ChengXiang Zhai is an Associate Professor of Computer Science at the University of Illinois at Urbana-Champaign, where he also holds a joint appointment at the Institute for Genomic Biology, Statistics, and the Graduate School of Library and Information Science. He received a Ph.D. in Computer Science from Nanjing University in 1990, and a Ph.D. in Language and Information Technologies from Carnegie Mellon University in 2002. He worked at Clairvoyance Corp. as a Research Scientist and a Senior Research Scientist from 1997 to 2000. His research interests include information retrieval, text mining, natural language processing, machine learning, and bioinformatics. He is an Associate Editor of ACM Transactions on Information Systems, and Information Processing and Management, and serves on the editorial board of Information Retrieval Journal. He is a program co-chair of ACM CIKM 2004, NAACL HLT 2007, and ACM SIGIR 2009. He is an ACM Distinguished Scientist, and received the 2004 Presidential Early Career Award for Scientists and Engineers (PECASE), the ACM SIGIR 2004 Best Paper Award, an Alfred P. Sloan Research Fellowship in 2008, and an IBM Faculty Award in 2009.

Text information plays a very important role in our lives. Web pages, email messages, scientific literature, and office documents are good examples of text information that we encounter all the time. With the dramatic increase in online information in recent years, management of text information is becoming increasingly important; for example, Web search engines are now being used by all of us routinely to find information on the Web. The huge amount of information presents both challenges and opportunities. The challenge is how to manage large amounts of information effectively and efficiently so that we can easily find useful information. The opportunity is the possibility of exploiting statistical inference to discover knowledge ("hidden patterns"). Correspondingly, there are two broad directions -- intelligent information access (to address the challenge) and text data mining (to exploit the opportunity). There are two modes of information access -- "pull" and "push", depending on whether the user initiates the process. In the pull mode, a user searches for information by using a search engine (e.g., Google) or browes information items through structures available on the information space (e.g., Yahoo directory). In the push mode, an information management system keeps track of a user's interest and recommends any relevant incoming information items to a user.

• Erik Cambria, National University of Singapore (Singapore)
• Bing Liu, University of Illinois at Chicago (USA)
• Yongzheng Zhang, eBay Inc. (USA)
• Yunqing Xia, Tsinghua University (China)

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