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Mon, 19 Aug 2013 16:58:43 +0800
Erik Cambria <[log in to unmask]>
ACM SIGMM Interest List <[log in to unmask]>
Erik Cambria <[log in to unmask]>
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Apologies for cross-posting,

The deadline of the Elsevier Neural Networks special issue on Affective and Cognitive Learning Systems for Big Social Data Analysis has been extended to 30th August. 
For more/up-to-date info, please visit

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 multidimensional 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.

August 30th, 2013: Paper submission deadline
November 30th, 2013: Notification of acceptance
December 31st, 2013: Final manuscript due
April/May, 2014: Publication

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.

 Amir Hussain, University of Stirling (UK)
 Erik Cambria, National University of Singapore (Singapore)
 Bjoern Schuller, Technical University of Munich (Germany)
 Newton Howard, MIT Media Laboratory (USA)

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