[Corpora-List] CFP: IEEE Computational Intelligence Magazine (Impact Factor: 4.629)

Arturo Montejo Raez amontejo at ujaen.es
Mon Jul 1 12:42:25 CEST 2013

Hi Erik,

This call sounds very interesting. We will submit the refined version of our method.

One question: you mention that the manuscript should be one-column, but the guide lines for authors in IEEE and the templates delivered exhibit a different format. Could you enlight me on this?


Arturo Montejo Ráez Dpto. de Informática E.P.S. Jaén - A3-114 Universidad de Jaén 23071 - Jaén (Spain) E-mail: amontejo at ujaen.es Web: http://wwwdi.ujaen.es/~amontejo Phone: +34 953 212882 Fax: +34 953 212472 [37°47'13.30"N, 3°46'39.00"W]

2013/7/1 Erik Cambria <cambria at nus.edu.sg>

> Apologies for cross-posting.
> Submissions are invited for a special issue on Computational Intelligence
> for Natural Language Processing of IEEE Computational Intelligence
> Magazine, which now has a 4.629 impact factor .
> Deadline for submission is in one month from today, no extensions will be
> granted. For more/up-to-date info, please visit http://sentic.net/cinlp
> 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 studies 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. Sentiment analysis grows out of this
> need: it is a very challenging NLP or text mining problem. Due to its
> tremendous value for practical applications, there has been an explosive
> growth of both research in academia and applications in the industry.
> All the sentiment analysis 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 relied a lot on traditional machine
> learning algorithms. Some of the most effective machine learning
> algorithms, however, produce no human understandable results. Apart from
> some superficial knowledge gained in the manual feature engineering
> process, in fact, such algorithms may achieve improved accuracy, but little
> about how and why is actually known. All such approaches, moreover, rely on
> syntactic structure of text, which is far from the way human mind processes
> natural language.
> Articles are thus invited in area of computational intelligence for
> natural language processing and understanding. The broader context of the
> Special Issue comprehends artificial intelligence, knowledge representation
> and reasoning, data mining, artificial neural networks, evolutionary
> computation, and fuzzy logic. Topics include, but are not limited to:
> - Computational intelligence for big social data analysis
> - Biologically inspired opinion mining
> - Concept-level opinion and sentiment analysis
> - Computational intelligence for social media retrieval and analysis
> - Computational intelligence for social media marketing
> - Social network modeling, simulation, and visualization
> - Semantic multi-dimensional scaling for sentiment analysis
> - Computational intelligence for patient opinion mining
> - Sentic computing
> - Multilingual and multimodal sentiment analysis
> - Multimodal fusion for continuous interpretation of semantics
> - Computational intelligence for time-evolving sentiment tracking
> - Computational intelligence for cognitive agent-based computing
> - Human-agent, -computer, and -robot interaction
> - Domain adaptation for sentiment classification
> - Affective common-sense reasoning
> - Computational intelligence for user profiling and personalization
> - Computational intelligence for knowledge acquisition
> August 1st, 2013: Paper submission deadline
> September 1st, 2013: Notification of acceptance
> October 1st, 2013: Final manuscript due
> February, 2014: Publication
> The maximum length for the manuscript is typically 25 pages in single
> column with double-spacing, including figures and references. Authors of
> papers should specify in the first page of their manuscripts corresponding
> author’s contact and up to 5 keywords. Submission should be made via email
> to one of the guest editors below.
> - Erik Cambria, National University of Singapore (Singapore)
> - Bebo White, Stanford University (USA)
> - Tariq S. Durrani, Royal Society of Edinburgh (UK)
> - Newton Howard, MIT Media Laboratory (USA)
> _______________________________
> Erik Cambria, PhD
> 康文涵
> Research Scientist
> Temasek Laboratories
> Cognitive Science Programme
> National University of Singapore
> 5A Engineering Drive 1, Singapore 117411
> Skype: senticnet
> Website: http://sentic.net
> Email: cambria at nus.edu.sg
> Twitter: http://twitter.com/senticnet
> Facebook: http://facebook.com/senticnet
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