Affective and Cognitive Learning Systems for Big Social Data Analysis
Guest Editors
Amir Hussain (Lead Guest Editor), University of Stirling, United Kingdom (ahu@cs.stir.ac.uk)
Erik Cambria, National University of Singapore, Singapore (cambria@nus.edu.sg)
Björn Schuller, Technische Universität München, Germany (schuller@tum.de)
Newton Howard, MIT Media Laboratory, USA (nhmit@mit.edu)
Amir Hussain (Lead Guest Editor), University of Stirling, United Kingdom (ahu@cs.stir.ac.uk)
Erik Cambria, National University of Singapore, Singapore (cambria@nus.edu.sg)
Björn Schuller, Technische Universität München, Germany (schuller@tum.de)
Newton Howard, MIT Media Laboratory, USA (nhmit@mit.edu)
Background and Motivation
As the Web rapidly evolves,Web users are evolving with it. In an era of social connectedness, people are becoming more and more enthusiastic about interacting, sharing, and collaborating through social networks, online communities, blogs, Wikis, and other online collaborative media. In recent years, this collective intelligence has spread to many different areas, with particular focus on fields related to everyday life such as commerce, tourism, education, and health, causing the size of the Web to expand exponentially. The distillation of knowledge from such a large amount of unstructured information, however, is an extremely difficult task, as the contents of today’s Web are perfectly suitable for human consumption, but remain hardly accessible to machines. The opportunity to capture the opinions of the general public about social events, political movements, company strategies, marketing campaigns, and product preferences 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 market prediction.
Existing approaches to opinion mining mainly rely on parts of text
in which sentiment is explicitly expressed, e.g., through polarity terms
or 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 techniques that further develop and apply big
data analysis tools and techniques for sentiment analysis. A key
motivation for this Special Issue, in particular, is to explore the
adoption of novel affective and cognitive learning systems to go beyond a
mere word-level analysis of natural language text and provide novel
concept-level tools and techniques that allow a more efficient passage
from (unstructured) natural language to (structured) machine-processable
data, in potentially any domain.
Articles are thus invited in areas such as machine learning, weakly
supervised learning, active learning, transfer learning, deep neural
networks, 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:
• Machine learning for big social data analysis
• Biologically inspired opinion mining
• Semantic multi-dimensional scaling for sentiment analysis
• Social media marketing
• Social media analysis, representation, and retrieval
• Social network modeling, simulation, and visualization
• Concept-level opinion and sentiment analysis
• Patient opinion mining
• Sentic computing
• Multilingual sentiment analysis
• Time-evolving sentiment tracking
• Cross-domain evaluation
• Domain adaptation for sentiment classification
• Multimodal sentiment analysis
• Multimodal fusion for continuous interpretation of semantics
• Human-agent, -computer, and -robot interaction
• Affective common-sense reasoning
• Cognitive agent-based computing
• Image analysis and understanding
• User profiling and personalization
• Affective knowledge acquisition for sentiment analysis
The Special Issue also welcomes papers on specific application domains of big social data analysis, e.g., influence networks, customer experience management, intelligent user interfaces, multimedia management, computer-mediated human-human communication, enterprise feedback management, surveillance, art. The authors will be required to follow the Author’s Guide for manuscript submission to Elsevier Neural Networks.
Timeframe
Call for Papers out: April 2013
Submission Deadline: August 1, 2013
Notification of Acceptance: November 1, 2013
Final Manuscripts Due: December 1, 2013
Date of Publication: March 2014
Composition and Review Procedures
The Elsevier Neural Networks Special Issue on Affective and Cognitive Learning Systems for Big Social Data Analysis 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. All articles are expected to successfully negotiate the standard review procedures for Elsevier Neural Networks. Authors are required to follow Elsevier Neural Networks proceedings templates and to submit their manuscripts at http://ees.elsevier.com/neunet
