| Title | Sentic Computing |
| Speaker | Dr. Erik Cambria |
| Chair | David Powers |
Abstract
The focus of the proposed 2-hour tutorial is sentic computing [1], a multi-disciplinary approach to sentiment analysis at the crossroads between affective computing and commonsense computing, which exploits both computer and social sciences to better recognise, interpret, and process opinions and sentiments over the Web. The main aim of the tutorial is to discuss ways to further develop and apply publicly available1 sentic computing resources for the development of applications in fields such as big social data analysis [2], human-computer interaction [3], and e-health [4]. To this end, the tutorial will provide means to efficiently handle sentic computing models, e.g., the Hourglass of Emotions [5], techniques, e.g., sentic activation [6], tools, e.g., IsaCore [7], and services, e.g., Sentic API2 [8]. The tutorial will also include insights resulting from the forthcoming IEEE Intelligent System Special Issue on Concept-Level Opinion and Sentiment Analysis3 and a hands-on session to illustrate how to build a sentic-computing-based opinion mining engine step-by-step.
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 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.
1 http://sentic.net/downloads
2 http://sentic.net/api
3 http://computer.org/intelligent/cfp2
Biography
Erik Cambria (cambria@media.mit.edu) received his BEng and MEng with honours in Electronic Engineering from the University of Genova, in 2005 and 2008 respectively. In 2011, he has been awarded a PhD in Computing Science and Mathematics, following the completion of an industrial Cooperative Awards in Science and Engineering (CASE) research project, funded by the UK Engineering and Physical Sciences Research Council (EPSRC), which was born from the collaboration between the University of Stirling and the MIT Media Laboratory. Today, Erik is the lead investigator of Singapore MINDEF-funded project on Commonsense Knowledge Representation & Reasoning at Temasek Laboratories (National University of Singapore) and an associate researcher at the MIT Media Laboratory (Synthetic Intelligence Project). His interests include AI, Semantic Web, KR, NLP, opinion mining and sentiment analysis, affective and cognitive modelling, intention awareness, HCI, and e-health. Erik is also chair of several international conferences, e.g., Brain Inspired Cognitive Systems (BICS), symposia, e.g., Extreme Learning Machines (ELM), and workshop series, e.g., ICDM SENTIRE, KDD WISDOM, and WWW MABSDA. He is editorial board of Springer Cognitive Computation, guest editor of IEEE Intelligent Systems, and reviewer of other leading AI journals. He is a fellow of the Brain Sciences Foundation, the National Laboratory of Pattern Recognition (NLPR – Institute of Automation, Chinese Academy of Sciences), the National Taiwan University, Microsoft Research Asia, and HP Labs India.
