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Subject: [ E-CFP ] CfP Special Issue of IEEE Intelligent Systems Magazine on Concept-Level Opinion and Sentiment Analysis
From: <schuller_(on)_tum.de>
Date received: 13 Dec 2011
Deadline: 01 Jul 2012
Start date: -

Dear List,

For those of you interested:


Call for Papers

Special Issue of IEEE Intelligent Systems Magazine


Concept-Level Opinion and Sentiment Analysis

Submission deadline: 1 July 2012 Publication: March/April 2013

Opinions play a primary role in decision-making processes.
Whenever people need to make a choice, they are naturally
inclined to hear others' opinions. In particular, when the
decision involves consuming valuable resources, such as time
and/or money, people strongly rely on their peers' past
experiences. Just a few years ago, the main sources for
collecting such information were friends, acquaintances and, in
some cases, specialized magazines or websites. The passage from a
read-only to a read-write Web has provided people with new tools
that allow them to create and share, in a timely and
cost-efficient way, their own contents, ideas, and opinions with
virtually millions of people connected to the World Wide Web. The
opportunity to capture the opinions of the general public about
social events, political movements, company strategies, marketing
campaigns, and product preferences has raised more and more
interest both in the scientific community, for the exciting
emergent challenges, and in the business world, for the
remarkable fallouts in marketing and financial market prediction.

Mining opinions and sentiments from natural language, however, is
an extremely difficult task: it involves a deep understanding of
most of the explicit and implicit, regular and irregular,
syntactical and semantic rules of a language. Existing approaches
mainly rely on parts of text in which opinions and sentiments are
explicitly expressed such as polarity terms, 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 approaches to opinion
mining and sentiment analysis that are not entirely based on
domain-dependent corpora but also on general-purpose semantic
knowledge bases. The main motivation for the issue, in
particular, is to go beyond a mere word-level analysis of text
and provide novel concept-level approaches to opinion mining and
sentiment analysis that allow a more efficient passage from
(unstructured) textual information to (structured)
machine-processible data, in potentially any domain.

Articles are thus invited in areas such as AI, the Semantic Web,
knowledge-based systems, and adaptive and transfer learning for
research on opinion and sentiment retrieval and analysis.
Potential topics include

* Opinion and sentiment summarization and visualization
* Explicit and latent semantic analysis for opinion and sentiment
* Knowledge base construction and integration with opinion and
  sentiment analysis
* Transfer learning of opinion and sentiment with knowledge bases
* Time-evolving opinion and sentiment analysis
* Corpora and resources for opinion and sentiment analysis
* Multimodal sentiment analysis
* Multidomain and cross-domain evaluation
* Multilingual sentiment analysis and reuse of knowledge bases


Guest Editors

Erik Cambria, National University of Singapore, Singapore;

Björn Schuller, Technische Universität München, Germany;

Bing Liu, University of Illinois at Chicago, USA; liub_(at)_cs.uic.edu

Haixun Wang, Microsoft Research Asia, China;

Catherine Havasi, MIT Media Laboratory, USA; havasi_(at)_media.mit.edu


Submission Guidelines

The special issue will consist of papers on novel methods and
techniques for building and using semantic knowledge bases in the
field of opinion mining and sentiment analysis. Besides the
specified topics of interest, the special issue also welcomes
papers on specific application domains of sentiment analysis-for
example, social data mining, influence networks, customer
experience management, computer-mediated human-human
communication, social media marketing, multimedia management,
personalization and persuasion, enterprise feedback management,
human-agent, -computer and -robot interaction, intelligent user
interfaces, patient opinion mining, surveillance, and art.

Submissions should be 3,000 to 5,400 words (counting a standard
figure or table as 200 words) and should follow IEEE Intelligent
Systems style and presentation guidelines
(www.computer.org/intelligent/author). The manuscripts cannot
have been published or be currently submitted for publication
elsewhere. We strongly encourage submissions that include audio,
video, and community content, which will be featured on the IEEE
Computer Society Web site along with the accepted papers.


. For general information about the special issue, contact Erik
  Cambria (include the keyword "concept-level sentiment analysis"
  in the subject line) at cambria_(at)_nus.edu.sg.
. For general author guidelines, see
. For submission details, see intelligent_(at)_computer.org.
. To submit an article, go to
  https://mc.manuscriptcentral.com/is-cs (log in and then select
  "Special Issue on Concept-Level Sentiment Analysis").

Thank you for excusing cross-postings.


Dr. Björn Schuller

Technische Universität München Institute for Human-Machine
Communication D-80333 München Germany +49-(0)89-289-28548

schuller_(at)_tum.de www.mmk.ei.tum.de/~sch


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