MABSDA 2015 - 2nd Multidisciplinary Approaches to Big Social Data Analysis (MABSDA)

As the Web rapidly evolves, Web users are evolving with it. In the 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.
MABSDA 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 the workshop comprehends information retrieval, natural language processing, web mining, semantic web, and artificial intelligence. 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
Opinion spam detection
Paper submission and reviewing will be handled electronically via EasyChair. Submissions should be formatted in the UAI format and papers (including figures and text) are limited to 9 pages in length. An additional 10th page is allowed containing only references. Optional submissions of supplementary materials are allowed. However, reviewers are under no obligation to look at the submitted supplementary materials, and will base their review primarily on the main paper. Papers that are currently under review or have already been accepted or published in a refereed venue, including conferences and journals, may not be submitted. Authors are strongly encouraged to make data and code publicly available when possible. The review process is double blind. Please make sure that the submission does not disclose the author's identities or affiliation. Selected, expanded versions of papers presented at the workshop will be invited to a forthcoming Special Issue of Cognitive Computation on opinion mining and sentiment analysis.
Organizers:
Erik Cambria, Nanyang Technological University (Singapore)
Yunqing Xia, Tsinghua University (China)
Newton Howard, MIT Media Laboratory (USA)