As the Web rapidly evolves, Web users are evolving with it. In an era of social connectedness, people are becoming increasingly 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 Social 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.
The main aim of AI4BigData is to explore the new frontiers of big data computing for opinion mining and sentiment analysis through machine learning techniques, knowledge-based systems, adaptive and transfer learning, in order to more efficiently retrieve and extract social information from the Web.
The special track aims to provide an international forum for researchers in the field of big data computing for opinion mining and sentiment analysis to share information on their latest investigations in social information retrieval and their applications both in academic research areas and industrial sectors. The broader context of AI4BigData comprehends AI, information retrieval, natural language processing, and web mining. Topics of interest include but are not limited to:
• Machine learning for sentiment mining
• Concept-level sentiment analysis
• Biologically-inspired opinion mining
• Sentiment identification & classification
• Association rule learning for opinion mining
• Time evolving opinion & sentiment analysis
• Multi-modal sentiment analysis
• Multi-domain & cross-domain evaluation
• Knowledge base construction & integration with opinion analysis
• Transfer learning of opinion & sentiment with knowledge bases
• Sentiment topic detection & trend discovery
• Social ranking
• Social network analysis
• Human computation
• Opinion spam detection
The special track also welcomes papers on specific application domains of knowledge-based systems for big data analysis, e.g., influence networks, customer experience management, intelligent user interfaces, multimedia management, computer-mediated human-human communication, enterprise feedback management, surveillance, and art.
AI4BigData'16 (AAAI FLAIRS-29, 17th May 2016, Key Largo, Florida, USA)
Submissions are invited to AI4BigData'16 to be held at AAAI FLAIRS-29 at Key Largo on 16th May 2016.
November 16th, 2015: Paper submission deadline
January 18th, 2016: Notification of paper acceptance
February 22nd, 2016: Camera-ready of accepted papers
May 17th, 2016: Special track date
SUBMISSION AND PROCEEDINGS
Submitted papers must be original, and not submitted concurrently to a journal or another conference. Double-blind reviewing will be provided, so submitted papers must use fake author names and affiliations. Papers must use the latest AAAI Press template, and must be submitted as PDF through EasyChair. There are three kinds of submissions: full papers (up to 6 pages), short papers (up to 4 pages), and poster abstracts (up to 250 words). Acceptance as a full paper entails a 20 minute presentation during a regular session, while short papers and abstracts will be required to participate in the poster session. Rejected full papers may still be accepted as short papers or poster abstracts. Selected, expanded versions of Special Track papers will be published in a follow-on Special Issue of Springer's Cognitive Computation journal.
• Erik Cambria, Nanyang Technological University (Singapore)
• Viviana Patti, University of Turin (Italy)
• Amir Hussain, University of Stirling (UK)
• Newton Howard, MIT Media Laboratory (USA)
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