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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