Appel: Sentic Computing @ Springer CogComp, Special issue on Sentic Computing

[Thierry Hamon]

Date: Thu, 16 Jan 2014 07:05:29 +0800
From: Erik Cambria <cambria at nus.edu.sg>
Message-ID: <CA42EAB9-8DD6-4F73-B41A-82241AC45475 at nus.edu.sg>
X-url: http://sentic.net/cogcomp


Apologies for cross-posting, 

Submissions are invited for a Springer Cognitive Computation special
issue on Sentic Computing. 
For more information, please visit http://sentic.net/cogcomp

RATIONALE 
The opportunity to capture the opinions of the general public has raised
growing interest both within the scientific community, leading to many
exciting open challenges, as well as in the business world, due to the
remarkable benefits to be had from marketing and financial
prediction. Mining opinions and sentiments from natural language,
however, is an extremely difficult task as it involves a deep
understanding of most of the explicit and implicit, regular and
irregular, syntactical and semantic rules proper of a language. Existing
approaches to sentiment analysis mainly rely on parts of text in which
opinions 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. 

Concept-level approaches, instead, use Web ontologies or semantic
networks to accomplish semantic text analysis. This helps the system
grasp the conceptual and affective information associated with natural
language opinions. By relying on large semantic knowledge bases, such
approaches step away from blindly using keywords and word co-occurrence
counts, and instead rely on the implicit meaning/features associated
with natural language concepts. Superior to purely syntactical
techniques, concept-based approaches can detect subtly expressed
sentiments. Concept-based approaches, in fact, can analyze multi-word
expressions that do not explicitly convey emotion, but are related to
concepts that do. 

Sentic computing, in particular, is a multi-disciplinary approach to
sentiment analysis at the crossroads between affective computing and
common sense computing, which exploits both computer and social sciences
to better recognize, interpret, and process opinions and sentiments over
the Web. In sentic computing, whose term derives from the Latin sentire
(root of words such as sentiment and sentience) and sensus (intended
both as capability of feeling and as common sense), the analysis of
natural language is based on affective ontologies and common sense
reasoning tools, which enable the analysis of text not only at
document-, page- or paragraph-level, but also at sentence-, clause-, and
concept-level. 

TOPICS 
This special issue focuses on the introduction, presentation, and
discussion of new approaches that further develop and apply sentic
computing models, techniques, and tools, for the design of
emotion-sensitive applications in fields such as social media marketing,
human-computer interaction, and e-health. The main motivation for the
special issue, in particular, is to further explore how the passage from
(unstructured) natural language to (structured) machine-processable data
can be implemented, in potentially any domain, through the application
of sentic computing or an ensemble of sentic computing and other
approaches. Articles are thus invited in areas such as weakly supervised
learning, active learning, transfer learning, novel neural and cognitive
models, data mining, pattern recognition, knowledge-based systems,
information retrieval, natural language processing, and big data
computing. Topics include, but are not limited to: 

- Sentic computing for social media marketing 
- Sentic computing for big social data analysis 
- Sentic computing for social media visualization and retrieval 
- Sentic computing for biologically inspired opinion mining 
- Sentic computing for cognitive and affective modeling 
- Sentic computing for metaphor detection and understanding 
- Sentic computing for patient opinion mining 
- Sentic computing for opinion spam detection 
- Sentic computing for online advertising 
- Sentic computing for social network modeling and analysis 
- Sentic computing for multi-modal sentiment analysis 
- Sentic computing for human-agent, -computer, and -robot interaction 
- Sentic computing for image analysis and understanding 
- Sentic computing for user profiling and personalization 
- Sentic computing for aided affective knowledge acquisition 
- Sentic computing for multi-lingual sentiment analysis 
- Sentic computing for time-evolving sentiment tracking 
- Sentic computing for cross-domain evaluation 

The special issue also welcomes papers on specific application domains
of sentic computing, e.g., influence networks, customer experience
management, intelligent user interfaces, multimedia management,
computer-mediated human-human communication, enterprise feedback
management, surveillance, and art. To be considered, authors will need
to clearly establish relevance of their paper to the scope of the
special issue and the journal. Authors will be required to follow the
Author's Guide for manuscript submission to Cognitive Computation. 


TIMEFRAME 
February 15th, 2014: Paper submission deadline 
March 15th, 2014: Notification of acceptance 
April 15th, 2014: Final manuscript due 
June, 2014: Publication 

SUBMISSION GUIDELINES 
The Cognitive Computation special issue on Sentic Computing will consist
of papers on novel methods and techniques that further develop and apply
big data analysis tools and techniques in the context of opinion mining
and sentiment analysis. Some papers may survey various aspects of the
topic. The balance between these will be adjusted to maximize the
issue's impact. Authors are required to follow Cognitive Computation's
Instructions for Authors and to submit their papers through Editorial
Manager, after specifing the name of the special issue. All articles are
expected to successfully negotiate the standard review procedures for
Cognitive Computation. 

ORGANIZERS 
- Erik Cambria, National University of Singapore (Singapore) 
- Amir Hussain, University of Stirling (UK)