[Elsnet-list] CfP Special Issue of Neural Networks on Affective and Cognitive Learning Systems for Big Social Data Analysis

Bjoern Schuller schuller at tum.de
Mon Apr 22 01:21:56 CEST 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 fields 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 difficult 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 scientific community, leading to many exciting open challenges, as well as in the business world, due to the remarkable benefits to be had from marketing and financial 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 efficient 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 classification
• 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 profiling and personalization
• Affective knowledge acquisition for sentiment analysis
The Special Issue also welcomes papers on specific application domains of big social data
analysis, e.g., influence 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
Notification 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
___________________________________________

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