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Subject: [ E-CFP ] CFP: Elsevier KBS special issue on Big Data for Social Analysis
From: <cambria_(on)_nus.edu.sg>
Date received: 01 Sep 2013
Deadline: 01 Nov 2013
Start date: -





Apologies for cross-posting,

Submissions are invited for an Elsevier Knowledge-Based Systems
special issue on Big Data for Social Analysis.

RATIONALE The textual information available on the Web can be
broadly grouped into two main categories: facts and opinions.
Facts are objective expressions about entities or events.
Opinions are usually subjective expressions that describe
people's sentiments, appraisals, or feelings towards such
entities and events. Much of the existing research on textual
information processing has been focused on mining and retrieval
of factual information, e.g., text classification, text
recognition, text clustering, and many other text mining and
natural language processing (NLP) tasks. Little work had been
done on the processing of opinions until only recently.

One of the main reasons for the lack of study on opinions is the
fact that there was little opinionated text available before the
recent passage from a read-only to a read-write Web. Before that,
in fact, when people needed to make a decision, they typically
asked for opinions from friends and family. Similarly, when
organizations wanted to find the opinions or sentiments of the
general public about their products and services, they had to
specifically ask people by conducting opinion polls and surveys.

However, with the advent of the Social Web, the way people
express their views and opinions has dramatically changed. They
can now post reviews of products at merchant sites and express
their views on almost anything in Internet forums, discussion
groups, and blogs. Such online word-of-mouth behavior represents
new and measurable sources of information with many practical
applications. Nonetheless, finding opinion sources and monitoring
them can be a formidable task because there are a large number of
diverse sources and each source may also have a huge volume of
opinionated text.

In many cases, in fact, opinions are hidden in long forum posts
and blogs. It is extremely time-consuming for a human reader to
find relevant sources, extract related sentences with opinions,
read them, summarize them, and organize them into usable forms.
Thus, automated opinion discovery and summarization systems are
needed. Big social data analysis grows out of this need and it
includes disciplines such as social network analysis, multimedia
management, social media analytics, trend discovery, and opinion
mining. The opportunity to capture the opinions of the general
public about social events, political movements, company
strategies, marketing campaigns, and product preferences, in
particular, has raised growing interest both within the
scientific community.

All the opinion-mining tasks, however, are very challenging. Our
understanding and knowledge of the problem and its solution are
still limited. The main reason is that it is a NLP task, and NLP
has no easy problems. Another reason may be due to our popular
ways of doing research. So far, in fact, researchers have
probably relied too much on traditional machine-learning
algorithms. Some of the most effective machine-learning
algorithms, in fact, produce no human understandable results such
that, although they may achieve improved accuracy, little about
how and why is known, apart from some superficial knowledge
gained in the manual feature engineering process. All such
approaches, moreover, rely on syntactical structure of text,
which is far from the way human mind processes natural language.

TOPICS Articles are thus invited in area of knowledge-based
systems for big social data analysis. The broader context of the
Special Issue comprehends artificial intelligence, knowledge
representation and reasoning, natural language processing, and
data mining. Topics include, but are not limited to: 
Knowledge-based systems for big social data analysis 
Biologically inspired opinion mining  Concept-level opinion and
sentiment analysis  Knowledge-based systems for social media
retrieval and analysis  Knowledge-based systems for social media
marketing  Social network modeling, simulation, and
visualization  Semantic multi-dimensional scaling for sentiment
analysis  Knowledge-based systems for patient opinion mining 
Sentic computing  Multilingual and multimodal sentiment analysis
 Multimodal fusion for continuous interpretation of semantics 
Knowledge-based systems for time-evolving sentiment tracking 
Knowledge-based systems for cognitive agent-based computing 
Human-agent, -computer, and -robot interaction  Domain
adaptation for sentiment classification  Affective common-sense
reasoning  Knowledge-based systems for user profiling and
personalization

The Special Issue also welcomes papers on specific application
domains of knowledge-based systems for 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.

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

SUBMISSION AND PROCEEDINGS The Special Issue will consist of
papers on novel methods and approaches that further develop and
apply knowledge-based techniques in the context of natural
language processing and big social data 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 Knowledge-Based Systems. Contributions are invited
in the form of original high-quality research and review papers
(preferably no more than 20 double line spaced manuscript pages,
including tables and figures), following the formatting style for
Elsevier. A submission that has already been published in
conference proceedings has to be submitted as more than 45%
update in comparison to the published version. The title page
should not include name, affiliation, and e-mail address of the
authors. All paper has to be submitted through thejournal
electronic submission EES via the dedicated special issue.

ORGANIZERS  Erik Cambria, National University of Singapore
(Singapore)  Haixun Wang, Google Research (USA)  Bebo White,
Stanford University (USA)

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