Connectionists: Call for Papers: IEEE Transactions on Affective Computing Special Issue: "Affective Reasoning for Big Social Data Analysis"

[Dr Amir Hussain]

IEEE Transactions on Affective Computing (
http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5165369 - ISI-SCI
Impact Factor: 1.87)
invites papers for a new Special Issue on: "Affective Reasoning for Big
Social Data Analysis"

Full Call for Papers available below, and also online:
https://www.computer.org/cms/Computer.org/transactions/cfps/cfp_tacsi_arbsda.pdf

Guest Editors
Erik Cambria, Nanyang Technological University, Singapore (
cambria at ntu.edu.sg)
Amir Hussain, University of Stirling, United Kingdom (ahu at cs.stir.ac.uk)
Alessandro Vinciarelli, University of Glasgow, United Kingdom (
vincia at dcs.gla.ac.uk)

Important Dates:
Submission Deadline: December 1st, 2016
Notification of Acceptance: Feburary 1st, 2017
Revised submission Deadline: April 1st, 2017
Final Manuscripts Due: June 1st, 2017

Background and Motivation:
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
• Multi-modal sentiment analysis
• Affective human-agent, -computer, and -robot interaction
• 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 ToAC.

-- 
The University achieved an overall 5 stars in the QS World University Rankings 2015
The University of Stirling is a charity registered in Scotland, 
 number SC 011159.

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