Appel: Special issue on Affective and Cognitive Learning Systems for Big Social Data Analysis, Elsevier Neural Networks

[Thierry Hamon]

Date: Thu, 18 Apr 2013 18:21:59 +0800
From: Erik Cambria <cambria at nus.edu.sg>
Message-ID: <98488E1D-528C-44D7-BFEC-6BF0CF9A264A at nus.edu.sg>
X-url: http://sentic.net/affcog


Apologies for cross-posting.

Submissions are invited for an Elsevier Neural Networks special issue on
Affective and Cognitive Learning Systems for Big Social Data
Analysis. For more/up-to-date info, please visit
http://sentic.net/affcog


ABSTRACT
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.


TOPICS
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.

TIMEFRAME
August 1st, 2013: Paper submission deadline
November 1st, 2013: Notification of acceptance
December 1st, 2013: Final manuscript due
March/April, 2014: Publication

SUBMISSION AND PROCEEDINGS
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.


ORGANIZERS
- 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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