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Category:   E-CFP
Subject:   Special Issue of Neural Networks on Affective and Cognitive Learning Systems for Big Social Data Analysis
Email:   schuller_(on)_tum.de
Date received:   22 Apr 2013
Deadline:   01 Aug 2013

Dear Colleagues, In case you should be interested, please find below a Call for Papers for a Special Issue of Neural Networks (Elsevier) on Affective and Cognitive Learning Systems for Big Social Data Analysis http://www.journals.elsevier.com/neural-networks/call-for-papers/affective-and-cognitive-learning-systems-for-big-social-data/ Guest Editors Amir Hussain*, University of Stirling, United Kingdom (ahu_(at)_cs.stir.ac.uk) Erik Cambria, National University of Singapore, Singapore (cambria_(at)_nus.edu.sg) Björn Schuller, Technische Universität München, Germany (schuller_(at)_tum.de) Newton Howard, MIT Media Laboratory, USA (nhmit_(at)_mit.edu) Background and Motivation As the Web rapidly evolves, Web users are evolving with it. In an era of social connectedness, people are becoming more and more enthusiastic about interacting, sharing, and collaborating through social networks, online communities, blogs, Wikis, and other online collaborative media. In recent years, this collective intelligence has spread to many different areas, with particular focus on ?elds related to everyday life such as commerce, tourism, education, and health, causing the size of the Web to expand exponentially. The distillation of knowledge from such a large amount of unstructured information, however, is an extremely dif?cult task, as the contents of today's Web are perfectly suitable for human consumption, but remain hardly accessible to machines. The opportunity to capture the opinions of the general public about social events, political movements, company strategies, marketing campaigns, and product preferences has raised growing interest both within the scienti?c community, leading to many exciting open challenges, as well as in the business world, due to the remarkable bene?ts to be had from marketing and ?nancial market prediction. Existing approaches to opinion mininig mainly rely on parts of text in which sentiment is explicitly expressed, e.g., through polarity terms or affect words (and their co-occurrence frequencies). However, opinions and sentiments are often conveyed implicitly through latent semantics, which make purely syntactical approaches ineffective. In this light, this Special Issue focuses on the introduction, presentation, and discussion of novel techniques that further develop and apply big data analysis tools and techniques for sentiment analysis. A key motivation for this Special Issue, in particular, is to explore the adoption of novel affective and cognitive learning systems to go beyond a mere word-level analysis of natural language text and provide novel concept-level tools and techniques that allow a more ef?cient passage from (unstructured) natural language to (structured) machine-processable data, in potentially any domain. Articles are thus invited in areas such as machine learning, weakly supervised learning, active learning, transfer learning, deep neural networks, novel neural and cognitive models, data mining, pattern recognition, knowledge-based systems, information retrieval, natural language processing, and big data computing. Topics include, but are not limited to:  Machine learning for big social data analysis  Biologically inspired opinion mining  Semantic multi-dimensional scaling for sentiment analysis  Social media marketing  Social media analysis, representation, and retrieval  Social network modeling, simulation, and visualization  Concept-level opinion and sentiment analysis  Patient opinion mining  Sentic computing  Multilingual sentiment analysis  Time-evolving sentiment tracking  Cross-domain evaluation  Domain adaptation for sentiment classi?cation  Multimodal sentiment analysis  Multimodal fusion for continuous interpretation of semantics  Human-agent, -computer, and -robot interaction  Affective common-sense reasoning  Cognitive agent-based computing  Image analysis and understanding  User pro?ling and personalization  Affective knowledge acquisition for sentiment analysis The Special Issue also welcomes papers on speci?c application domains of big social data analysis, e.g., in?uence networks, customer experience management, intelligent user interfaces, multimedia management, computer-mediated human-human communication, enterprise feedback management, surveillance, art. The authors will be required to follow the Author's Guide for manuscript submission to Elsevier Neural Networks. Timeframe Call for Papers out: April 2013 Submission Deadline: August 1st, 2013 Noti?cation of Acceptance: November 1st, 2013 Final Manuscripts Due: December 1st, 2013 Date of Publication: March 2014 Composition and Review Procedures The Elsevier Neural Networks Special Issue on Affective and Cognitive Learning Systems for Big Social Data Analysis will consist of papers on novel methods and techniques that further develop and apply big data analysis tools and techniques in the context of opinion mining and sentiment analysis. Some papers may survey various aspects of the topic. The balance between these will be adjusted to maximize the issue's impact. All articles are expected to successfully negotiate the standard review procedures for Elsevier Neural Networks. ___________________________________________ Univ.-Prof. Dr.-Ing. habil. Björn W. Schuller Head Institute for Sensor Systems University of Passau Passau / Germany Head Machine Intelligence & Signal Processing Group Institute for Human-Machine Communication Technische Universität München Munich / Germany CEO audEERING UG (haftungsbeschränkt) Gilching / Germany Visiting Professor School of Computer Science and Technology Harbin Institute of Technology Harbin / P.R. China Associate Institute for Information and Communication Technologies JOANNEUM RESEARCH Graz / Austria Associate Centre Interfacultaire en Sciences Affectives Université de Genève Geneva / Switzerland schuller_(at)_ieee.org http://www.schuller.it ___________________________________________ __________________________________________ - ELSNET mailing list Elsnet-list_(at)_elsnet.org - To manage your subscription go to: http://mailman.elsnet.org/mailman/listinfo/elsnet-list

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