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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
Guest Editors Amir
Hussain*, University of Stirling, United Kingdom (ahu@xxxxxxxxxxxxx) 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 ïelds 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 difïcult 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 scientiïc community, leading to many exciting open challenges, as well as in the business world, due
to the remarkable beneïts to be had from marketing and ïnancial 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 efïcient 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: â Machine learning for big social data analysis The Special Issue also welcomes papers on speciïc
application domains of big social data Timeframe Call for Papers out: April 2013 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.
BjÃrn W. Schuller Head
Institute for Sensor Systems University of Passau Passau / Germany Head
Machine Intelligence & Signal Processing Group Institute for Human-Machine Communication Technische UniversitÃt MÃnchen Munich / Germany CEO audEERING UG (haftungsbeschrÃnkt) 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 Università de GenÃve Geneva / Switzerland schuller@xxxxxxxx http://www.schuller.it ___________________________________________ |