SenticNet

KDD WISDOM

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.

RATIONALE
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.

TOPICS
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

TIMEFRAME
• May 26th, 2013: Submission deadline
• June 8th, 2013: Notification of acceptance
• June 18th, 2013: Final manuscripts due
• August 12th, 2013: Workshop date

SUBMISSIONS AND PROCEEDINGS
Authors are required to follow ACM SIG Proceedings Templates and to submit their papers through EasyChair. The paper length is limited to 9 pages, including references, diagrams, and appendices, if any. As per KDD tradition, reviews are not double-blind, and author names and affiliations should be listed. Each submitted paper will be evaluated by three PC members with respect to its novelty, significance, technical soundness, presentation, and experiments. Accepted papers will be published in ACM KDD 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.

2013 SPEAKER
ChengXiang Zhai is an Associate Professor of Computer Science at the University of Illinois at Urbana-Champaign, where he is also affiliated with the Graduate School of Library and Information Science, Institute for Genomic Biology, and Department of Statistics. 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 biomedical informatics, in which he published over 150 research papers. 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 a recipient of multiple best paper awards, Alfred P. Sloan Research Fellowship, IBM Faculty Award, HP Innovation Research Program Award, and the Presidential Early Career Award for Scientists and Engineers (PECASE).

2013 KEYNOTE (Statistical Methods for Integration and Analysis of Opinionated Text Data)
Opinionated text data such as blogs, forum posts, product reviews and online comments are increasingly available on the Web. They are very useful sources for public opinions about virtually any topics. However, because the opinions are scattered and abundant, it is a significant challenge for users to collect all the opinions about a topic and digest them efficiently. In this talk, I will present a suite of general statistical text mining methods that can help users integrate, summarize and analyze scattered online opinions to obtain actionable knowledge for decision making. Specifically, I will first present approaches to integration of scattered opinions by aligning them to a well-structured article or relevant ontology. Second, I will discuss several techniques for generating a concise opinion summary that can reveal the major sentiments and opinion points buried in large amounts of opinionated text data. Finally, I will present probabilistic general models for analyzing review data in depth to discover latent aspect ratings and relative weights placed by reviewers on different aspects. These methods are completely general and can thus help users integrate and analyze large amounts of online opinionated text data on any topic in any natural language.

2013 ORGANIZERS
• 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)

2012 PROGRAM
09:00-10:15: KEYNOTE
• Opening Remarks
• Detecting Fake Opinions in Social Media (Bing Liu)

10:15-10:30: Coffee Break

10:30-12:00: SESSION I
• A Bayesian Modeling Approach to Multi-Dimensional Sentiment Distributions Prediction (Yulan He)
• Transverse Subjectivity Classification (Dinko Lambov and Gaël Dias)
• Combining Lexicon and Learning based Approaches for Concept-Level Sentiment Analysis (Andrius Mudinas, Dell Zhang, and Mark Levene)

12:00-13:30: Lunch Break

13:30-15:15: SESSION II
• A Unified Graph Model for Chinese Product Review Summarization Using Richer Information (He Huang and Chunping Li)
• Retrieval Approach to Extract Opinions about People from Resource Scarce Language News Articles (Aditya Mogadala and Vasudeva Varma)
• A Generic Approach to Generate Opinion Lists of Phrases for Opinion Mining Applications (Sven Rill, Johannes Drescher, Dirk Reinel, Joerg Scheidt, Oliver Schuetz, Florian Wogenstein, and Daniel Simon)
• Finding Emotion in Image Descriptions (Morgan Ulinski, Victor Soto, and Julia Hirschberg)

15:15-15:30: Coffee Break

15:30-17:00: SESSION III
• Predicting Collective Sentiment Dynamics from Time-series Social Media (Le Nguyen, Pang Wu, William Chan, Wei Peng, and Ying Zhang)
• Crowdsourcing Recommendations from Social Sentiment (Yusheng Xie, Yu Cheng, Daniel Honbo, Kunpeng Zhang, Ankit Agrawal, and Alok Choudhary)
• Fast Learning for Sentiment Analysis on Bullying (Jun-Ming Xu, Xiaojin Zhu, and Amy Bellmore)
• Closing Remarks

2012 SPEAKER
Bing Liu is a professor of Computer Science at University of Illinois at Chicago (UIC). He received his PhD in Artificial Intelligence from the University of Edinburgh. Before joining UIC, he was with the National University of Singapore. His current research interests include opinion mining and sentiment analysis, Web mining, and data mining. He has published extensively in leading conferences and journals in these fields. He has also written a textbook titled “Web Data Mining: Exploring Hyperlinks, Contents and Usage Data” published by Springer. The second edition of the book came out in July 2011. On professional services, Liu has served as program chairs of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), IEEE International Conference on Data Mining (ICDM), ACM Conference on Web Search and Data Mining (WSDM), SIAM Conference on Data Mining (SDM), ACM Conference on Information and Knowledge Management (CIKM), and Pacific Asia Conference on Data Mining (PAKDD). Additionally, he has also served as associate editors of IEEE Transactions on Knowledge and Data Engineering (TKDE), Journal of Data Mining and Knowledge Discovery (DMKD), and SIGKDD Explorations, and is on the editorial boards of several other journals.

2012 KEYNOTE
Opinions from social media are increasingly used by individuals and organizations for making purchase decisions and making choices at elections and for marketing and product design. Positive opinions often mean profits and fames for businesses and individuals, which, unfortunately, give strong incentives for people to game the system by posting fake opinions or reviews to promote or to discredit some target products, services, organizations, individuals, and even ideas without disclosing their true intentions, or the person or organization that they are secretly working for. Such individuals are called opinion spammers and their activities are called opinion spamming. Opinion spamming about social and political issues can even be frightening as they can warp opinions and mobilize masses into positions counter to legal or ethical mores. It is safe to say that as opinions in social media are increasingly used in practice, opinion spamming will become more and more rampant and sophisticated, which presents a major challenge for their detection. However, they must be detected in order to ensure that the social media is a trusted source of public opinions, rather than is full of fake opinions, lies, and deceptions. In this talk, I will introduce this research topic and discuss some state-of-the-art opinion spam detection techniques.

2012 ORGANIZERS
• Erik Cambria, National University of Singapore (Singapore)
• Yongzheng Zhang, eBay Inc. (USA)
• Yunqing Xia, Tsinghua University (China)
• Newton Howard, MIT Media Laboratory (USA)