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[PVS] CFP: IEEE ICDM SENTIRE (deadline extended)

Apologies for cross-posting.

Submissions are invited for this year's IEEE ICDM Workshop on Sentiment Elicitation from Natural Text for Information Retrieval and Extraction (http://sentic.net/sentire).

Due to many challenging research problems and a wide variety of practical applications, opinion mining and sentiment analysis have become very active research areas in the last decade. Our understanding and knowledge of the problem and its solution are still limited as natural language understanding techniques are still pretty weak. Most of current research in sentiment analysis, in fact, merely relies on machine learning algorithms. Such algorithms, despite most of them being very effective, produce no human understandable results such that we know little about how and why output values are obtained. All such approaches, moreover, rely on syntactical structure of text, which is far from the way human mind processes natural language. Next-generation opinion mining systems should employ techniques capable to better grasp the conceptual rules that govern sentiment and the clues that can convey these concepts from realisation to verbalisation in the human mind. The main aim of SENTIRE is to explore the new frontiers of opinion mining and sentiment analysis by proposing novel techniques in fields such as AI, Semantic Web, knowledge-based systems, adaptive and transfer learning, in order to more efficiently retrieve and extract social information from the Web.

SENTIRE aims to provide an international forum for researchers in the field of opinion mining and sentiment analysis to share information on their latest investigations in social information retrieval and their applications both in academic research areas and industrial sectors. The broader context of the workshop comprehends Web mining, AI, Semantic Web, information retrieval and natural language processing. In addition to paper presentations, an invited talk by Professor Rada Mihalcea will stress the interdisciplinary challenges of opinion mining and sentiment analysis. Topics of interest include but are not limited to:
•  Sentiment identification & classification
•  Opinion and sentiment summarisation & visualisation
•  Explicit & latent semantic analysis for sentiment mining
•  Opinion and sentiment search & retrieval
•  Time evolving opinion & sentiment analysis
•  Multi-modal sentiment analysis
•  Multi-domain & cross-domain evaluation
•  Multi-lingual sentiment analysis & re-use of knowledge bases
•  Knowledge base construction & integration with opinion analysis
•  Transfer learning of opinion & sentiment with knowledge bases
•  Sentiment corpora & annotation
•  Affective knowledge acquisition & representation
•  Sentiment topic detection & trend discovery
•  Social ranking
•  Social network analysis
•  Comparative opinion analysis
•  Opinion spam detection

•  August 21st, 2012: Due date for workshop papers (extended)
•  October 1st, 2012: Notification of paper acceptance to authors
•  October 15th, 2012: Camera-ready deadline for accepted papers
•  December 10th, 2012: Workshop date

Papers submitted to this workshop must not have been accepted for publication elsewhere or be under review for another workshop, conference or journal. Papers can be either full research papers (10 pages) or short papers (6 pages) and must be formatted to IEEE Computer Society proceedings manuscript style.

Accepted papers will be published in IEEE ICDM workshop proceedings. Selected, expanded versions of papers presented at the workshop will be published in a follow-on Special Issue of Springer Cognitive Computation.

With more than 10,000 new videos posted online every day on social websites such as YouTube and Facebook, the internet is becoming an almost infinite source of information. One crucial challenge for the coming decade is to be able to harvest relevant information from this constant flow of multi-modal data. In this talk, the keynote speaker will introduce the task of multi-modal sentiment analysis, and present a method that integrates linguistic, audio, and visual features for the purpose of identifying sentiment in online videos. The invited speaker will first describe a novel dataset consisting of videos collected from the social media website YouTube and annotated for sentiment polarity. She will then show, through comparative experiments, that the joint use of visual, audio, and textual features greatly improves over the use of only one modality at a time. Finally, by running evaluations on datasets in English and Spanish, the keynote speaker will show that the method is portable and works equally well when applied to different languages.

Rada Mihalcea is an Associate Professor in the Department of Computer Science and Engineering at the University of North Texas. Her research interests are in computational linguistics, with a focus on lexical semantics, graph-based algorithms for natural language processing, and multilingual natural language processing. She serves or has served on the editorial boards of the Journals of Computational Linguistics, Language Resources and Evaluations, Natural Language Engineering, Research in Language in Computation, IEEE Transactions on Affective Computing, and Transactions of the Association for Computational Linguistics. She was a program co-chair for the Conference of the Association for Computational Linguistics (2011), and the Conference on Empirical Methods in Natural Language Processing (2009). She is the recipient of a National Science Foundation CAREER award (2008) and a Presidential Early Career Award for Scientists and Engineers (2009).

•  Alexandra Balahur, University of Alicante (Spain)
•  Sandra Baldassarri, University of Zaragoza (Spain)
•  Eva Cerezo, University of Zaragoza (Spain)
•  Praphul Chandra, HP Labs India (India)
•  Amitava Das, Norwegian University of Science and Technology (Norway)
•  Dipankar Das, Jadavpur University (India)
•  Sergio Decherchi, Italian Institute of Technology (Italy)
•  Rafael Del Hoyo, Aragon Institute of Technology (Spain)
•  Tariq Durrani, University of Strathclyde (UK)
•  Marco Grassi, Marche Polytechnic University (Italy)
•  Minlie Huang, Tsinghua University (China)
•  Isabelle Hupont, Aragon Institute of Technology (Spain)
•  Lillian Lee, Yahoo Labs (USA)
•  Shixia Liu, Microsoft Research Asia (China)
•  Saif Mohammad, National Research Council (Canada)
•  Muaz Niazi, Bahria University (Pakistan)
•  Paolo Rosso, Technical University of Valencia (Spain)
•  Bjoern Schuller, Technical University of Munich (Germany)
•  Stefano Squartini, Marche Polytechnic University (Italy)
•  Rui Xia, Nanjing University of Science and Technology (China)
•  Yongzheng Zhang, eBay Research Labs (USA)
•  Lei Zhang, University of Illinois at Chicago (USA)

•  Erik Cambria, National University of Singapore (Singapore)
•  Bing Liu, University of Illinois at Chicago (USA)
•  Yunqing Xia, Tsinghua University (China)
•  Catherine Havasi, MIT Media Laboratory (USA)
Erik Cambria, PhD
Research Scientist

Temasek Laboratories
Cognitive Science Programme
National University of Singapore
5A Engineering Drive 1, Singapore 117411

Web: http://sentic.net
Email: cambria@xxxxxxxxxx
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