- From: Erik Cambria <cambria@nus.edu.sg>
- Date: Thu, 16 Jan 2014 07:04:19 +0800
- To: undisclosed-recipients:;
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)
Received on Wednesday, 15 January 2014 23:04:56 UTC