22.5118, Calls: Computational Linguistics/ IEEE Intelligent Systems (Jrnl)

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LINGUIST List: Vol-22-5118. Mon Dec 19 2011. ISSN: 1069 - 4875.

Subject: 22.5118, Calls: Computational Linguistics/ IEEE Intelligent Systems (Jrnl)

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1)
Date: 19-Dec-2011
From: Bjoern Schuller [schuller at tum.de]
Subject: IEEE Intelligent Systems


-------------------------Message 1 ---------------------------------- 
Date: Mon, 19 Dec 2011 12:40:38
From: Bjoern Schuller [schuller at tum.de]
Subject: IEEE Intelligent Systems

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Full Title: IEEE Intelligent Systems 


Call Deadline: 01-Jul-2012 

Special Issue of IEEE Intelligent Systems Magazine 
Concept-Level Opinion and Sentiment Analysis
Submission deadline: 1 July 2012

http://www.computer.org/portal/web/computingnow/iscfp2

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 mining
* 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; 
cambria at nus.edu.sg

Björn Schuller, Technische Universität München, Germany; schuller at tum.de

Bing Liu, University of Illinois at Chicago, USA; liub at cs.uic.edu

Haixun Wang, Microsoft Research Asia, China; haixun.wang at microsoft.com

Catherine Havasi, MIT Media Laboratory, USA; havasi at media.mit.edu





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