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Subject: [ E-CFP ] Special Issue of Neural Networks on Affective and Cognitive Learning Systems for Big Social Data Analysis
From: <schuller_(on)_tum.de>
Date received: 22 Apr 2013
Deadline: 01 Aug 2013
Start date: -





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

___________________________________________

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