CfP Special Issue of IEEE Intelligent Systems Magazine on Concept-Level Opinion and Sentiment Analysis (Bjoern Schuller )


Subject: CfP Special Issue of IEEE Intelligent Systems Magazine on Concept-Level Opinion and Sentiment Analysis
From:    Bjoern Schuller  <schuller@xxxxxxxx>
Date:    Mon, 12 Dec 2011 23:40:20 +0000
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--_002_543D4FD247AF9C489398AAD86F8577FE377D279CBADWLRZSWMBX1ad_ Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Dear List, For those of you interested: _____________________________________________________________ Call for Papers=20 Special Issue of IEEE Intelligent Systems Magazine=20 On Concept-Level Opinion and Sentiment Analysis Submission deadline: 1 July 2012 Publication: March/April 2013 _____________________________________________________________ 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, s= uch as time and/or money, people strongly rely on their peers' past experie= nces. Just a few years ago, the main sources for collecting such informatio= n 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 n= ew tools that allow them to create and share, in a timely and cost-efficien= t way, their own contents, ideas, and opinions with virtually millions of p= eople connected to the World Wide Web. The opportunity to capture the opini= ons of the general public about social events, political movements, company= strategies, marketing campaigns, and product preferences has raised more a= nd more interest both in the scientific community, for the exciting emergen= t challenges, and in the business world, for the remarkable fallouts in mar= keting and financial market prediction. Mining opinions and sentiments from natural language, however, is an extrem= ely difficult task: it involves a deep understanding of most of the explici= t and implicit, regular and irregular, syntactical and semantic rules of a = language. Existing approaches mainly rely on parts of text in which opinion= s and sentiments are explicitly expressed such as polarity terms, affect wo= rds, and their co-occurrence frequencies. However, opinions and sentiments = are often conveyed implicitly through latent semantics, which make purely s= yntactical approaches ineffective. In this light, this special issue focuses on the introduction, presentation= , and discussion of novel approaches to opinion mining and sentiment analys= is that are not entirely based on domain-dependent corpora but also on gene= ral-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 th= at allow a more efficient passage from (unstructured) textual information t= o (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 a= nd 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 an= alysis * 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@xxxxxxxx= sg Bj=F6rn Schuller, Technische Universit=E4t M=FCnchen, Germany; schuller@xxxxxxxx= .de Bing Liu, University of Illinois at Chicago, USA; liub@xxxxxxxx Haixun Wang, Microsoft Research Asia, China; haixun.wang@xxxxxxxx Catherine Havasi, MIT Media Laboratory, USA; havasi@xxxxxxxx _____________________________________________________________ Submission Guidelines The special issue will consist of papers on novel methods and techniques fo= r building and using semantic knowledge bases in the field of opinion minin= g and sentiment analysis. Besides the specified topics of interest, the spe= cial issue also welcomes papers on specific application domains of sentimen= t analysis-for example, social data mining, influence networks, customer ex= perience management, computer-mediated human-human communication, social me= dia marketing, multimedia management, personalization and persuasion, enter= prise feedback management, human-agent, -computer and -robot interaction, i= ntelligent user interfaces, patient opinion mining, surveillance, and art. Submissions should be 3,000 to 5,400 words (counting a standard figure or t= able as 200 words) and should follow IEEE Intelligent Systems style and pre= sentation guidelines (www.computer.org/intelligent/author). The manuscripts= cannot have been published or be currently submitted for publication elsew= here. We strongly encourage submissions that include audio, video, and community = content, which will be featured on the IEEE Computer Society Web site along= with the accepted papers. Questions? . For general information about the special issue, contact Erik Cambria (in= clude the keyword "concept-level sentiment analysis" in the subject line) a= t cambria@xxxxxxxx . For general author guidelines, see www.computer.org/intelligent/author. . For submission details, see intelligent@xxxxxxxx . To submit an article, go to https://mc.manuscriptcentral.com/is-cs (log i= n and then select "Special Issue on Concept-Level Sentiment Analysis"). Thank you for excusing cross-postings. ___________________________________________ Dr. Bj=F6rn Schuller Technische Universit=E4t M=FCnchen Institute for Human-Machine Communication D-80333 M=FCnchen Germany +49-(0)89-289-28548 schuller@xxxxxxxx www.mmk.ei.tum.de/~sch ___________________________________________ --_002_543D4FD247AF9C489398AAD86F8577FE377D279CBADWLRZSWMBX1ad_ Content-Type: application/pdf; name="Opinion Mining CFP.pdf" Content-Description: Opinion Mining CFP.pdf Content-Disposition: attachment; filename="Opinion Mining CFP.pdf"; size=38731; creation-date="Mon, 12 Dec 2011 23:03:30 GMT"; modification-date="Mon, 12 Dec 2011 23:03:30 GMT" Content-Transfer-Encoding: base64 JVBERi0xLjQNJeLjz9MNCjExOSAwIG9iag08PC9MaW5lYXJpemVkIDEvTCAzODczMS9PIDEyMS9F IDI0NDQwL04gMi9UIDM2MzAzL0ggWyA5NTMgMzI4XT4+DWVuZG9iag0gICAgICAgICAgICAgICAg DQp4cmVmDQoxMTkgMzINCjAwMDAwMDAwMTYgMDAwMDAgbg0KMDAwMDAwMTQ1MyAwMDAwMCBuDQow MDAwMDAxNzQ3IDAwMDAwIG4NCjAwMDAwMDIxNDAgMDAwMDAgbg0KMDAwMDAwMjQ4OSAwMDAwMCBu 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maintained by:
DAn Ellis <dpwe@ee.columbia.edu>
Electrical Engineering Dept., Columbia University