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Thu, 24 Nov 2016 06:44:58 -0500
"ACM SIGCHI General Interest Announcements (Mailing List)" <[log in to unmask]>
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Apologies for cross-posting,

Submissions are invited for a special issue of the IEEE Transactions on
Affective Computing (IEEE TAC) on Affective Reasoning for Big Social Data
Analysis. For more information, please visit:

As the Web rapidly evolves, Web users are evolving with it. In an era of social
connectedness, people are becoming increasingly 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 big 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 big social data analysis 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 affective reasoning tools and techniques for big social data
analysis. A key motivation for this Special Issue, in particular, is to explore
the adoption of novel affective reasoning frameworks 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
affective 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, common-sense reasoning, and
big data computing. Topics include, but are not limited to:
• Machine learning for big social data analysis
• Affective common-sense reasoning
• Social network modeling and analysis
• Social media representation and retrieval
• Discovering conceptual primitives for sentiment analysis
• Affective human-agent, -computer, and-robot interaction
• Multi-modal sentiment analysis
• User profiling and personalization
• Aided affective knowledge acquisition
• Multi-lingual sentiment analysis
• Time-evolving sentiment tracking
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 IEEE TAC.

January 21st, 2017: Paper submission deadline (strict)
April 1st, 2017: Notification of acceptance
May 1st, 2017: Revised submission deadline
June 1st, 2017: Final manuscript due

The IEEE TAC 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 IEEE TAC.

• Erik Cambria, Nanyang Technological University (Singapore)
• Amir Hussain, University of Stirling (UK)
• Alessandro Vinciarelli, University of Glasgow (UK)

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