Appel: IEEE Computational Intelligence Magazine, Special issue on Computational Intelligence for Natural Language Processing

[Thierry Hamon]

Date: Thu, 23 May 2013 17:41:25 +0800
From: Erik Cambria <cambria at nus.edu.sg>
Message-ID: <FB945505-2698-4491-95E4-BA100A890DFF at nus.edu.sg>
X-url: http://sentic.net/cinlp

Apologies for cross-posting.

Submissions are invited for an IEEE Computational Intelligence Magazine
special issue on Computational Intelligence for Natural Language
Processing.
For more/up-to-date info, please visit http://sentic.net/cinlp

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 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.

TOPICS

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

TIMEFRAME
August 1st, 2013: Paper submission deadline
September 1st, 2013: Notification of acceptance
October 1st, 2013: Final manuscript due
February, 2014: Publication

SUBMISSION

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.

GUEST EDITORS
- 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)

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