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Sun, 1 Sep 2013 16:13:52 +0800
Erik Cambria <[log in to unmask]>
ACM SIGMM Interest List <[log in to unmask]>
Erik Cambria <[log in to unmask]>
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Apologies for cross-posting,

Submissions are invited for an Elsevier Knowledge-Based Systems special issue on Big Data for Social Analysis.

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 study 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. Big social data analysis grows out of this need and it includes disciplines such as social network analysis, multimedia management, social media analytics, trend discovery, and opinion mining. The opportunity to capture the opinions of the general public about social events, political movements, company strategies, marketing campaigns, and product preferences, in particular, has raised growing interest both within the scientific community.

All the opinion-mining 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 probably relied too much on traditional machine-learning algorithms. Some of the most effective machine-learning algorithms, in fact, produce no human understandable results such that, although they may achieve improved accuracy, little about how and why is known, apart from some superficial knowledge gained in the manual feature engineering process. All such approaches, moreover, rely on syntactical structure of text, which is far from the way human mind processes natural language.

Articles are thus invited in area of knowledge-based systems for big social data analysis. The broader context of the Special Issue comprehends artificial intelligence, knowledge representation and reasoning, natural language processing, and data mining. Topics include, but are not limited to:
 Knowledge-based systems for big social data analysis
 Biologically inspired opinion mining
 Concept-level opinion and sentiment analysis
 Knowledge-based systems for social media retrieval and analysis
 Knowledge-based systems for social media marketing
 Social network modeling, simulation, and visualization
 Semantic multi-dimensional scaling for sentiment analysis
 Knowledge-based systems for patient opinion mining
 Sentic computing
 Multilingual and multimodal sentiment analysis
 Multimodal fusion for continuous interpretation of semantics
 Knowledge-based systems for time-evolving sentiment tracking
 Knowledge-based systems for cognitive agent-based computing
 Human-agent, -computer, and -robot interaction
 Domain adaptation for sentiment classification
 Affective common-sense reasoning
 Knowledge-based systems for user profiling and personalization

The Special Issue also welcomes papers on specific application domains of knowledge-based systems for 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.

November 1st, 2013: Paper submission deadline
December 1st, 2013: Notification of acceptance
January 1st, 2013: Final manuscript due
March/April, 2014: Publication

The Special Issue will consist of papers on novel methods and approaches that further develop and apply knowledge-based techniques in the context of natural language processing and big social data 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 Knowledge-Based Systems. Contributions are invited in the form of original high-quality research and review papers (preferably no more than 20 double line spaced manuscript pages, including tables and figures), following the formatting style for Elsevier. A submission that has already been published in conference proceedings has to be submitted as more than 45% update in comparison to the published version. The title page should not include name, affiliation, and e-mail address of the authors. All paper has to be submitted through thejournal electronic submission EES via the dedicated special issue.

 Erik Cambria, National University of Singapore (Singapore)
 Haixun Wang, Google Research (USA)
 Bebo White, Stanford University (USA)

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