Conference Tutorial

"Sentic Computing"

By Erik Cambria Research Scientist at the National University of Singapore (Cognitive Science Programme, Temasek Laboratories) and Associate Researcher at the Massachusetts Institute of Technology (Synthetic Intelligence Project, Media Laboratory)

About the Tutor

ERIK CAMBRIA ( This email address is being protected from spambots. You need JavaScript enabled to view it. ) 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, SemanticWeb, KR, NLP, opinion mining and sentiment analysis, affective and cognitive modelling, intention awareness, HCI, and e-health. Erik is editorial board of Springer Cognitive Computation and 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 also 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.


Recent Activities by the Tutor

• Guest Editor, Concept-Level Opinion and Sentiment Analysis, Special Issue of IEEE
Intelligent Systems (2013)
• Organizer,Workshop on Multidisciplinary Approaches to Big Social Data Analysis (MABSDA),
WWWWorkshop (2013)
• Organizer, Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM),
ACM KDD Workshop (since 2012)
• Chair, Extreme Learning Machines (ELM), Symposium (since 2012)
• Chair, Brain Inspired Cognitive Systems (BICS), Conference (since 2012)
• Organizer, Sentiment Elicitation from Natural Text for Information Retrieval and Extraction
(SENTIRE), IEEE ICDMWorkshop Series (since 2011)
• Chair, Health Web Science, WebSci Workshop Series (since 2011)
• Guest Lecturer, WebSci@UHI, UHI Undergraduate Course (since 2010)


Aims and Scope

The focus of the proposed full-day tutorial is sentic computing [1], 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 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 available [2] sentic computing resources for the development of applications in fields such as big social data analysis [3], human-computer interaction [4], and e-health [5].

To this end, the tutorial will provide means to efficiently handle sentic computing models, e.g., the Hourglass of Emotions [6], techniques, e.g., sentic activation [7], tools, e.g., SenticNet [8] and IsaCore [9], and services, e.g., Sentic API [10]. The tutorial will also include insights resulting from the forthcoming IEEE Intelligent System Special Issue on Concept-Level Opinion and Sentiment Analysis [11] and a hands-on session to illustrate how to build a sentic-computing-based opinion mining engine step-by-step.

[1] Cambria, E. & Hussain, A. (2012). Sentic Computing: Techniques, Tools, and Applications, Springer: Dordrecht, Netherlands.

[2] SenticNet resources -

[3] Cambria, E., Grassi, M., Hussain, A. & Havasi, C. (2012). Sentic computing for social media marketing, Multimedia Tools and Applications 59(2): 557-577.

[4] Cambria, E. & Hussain, A. (2012). Sentic album: Content-, concept-, and context-based online personal photo management system, Cognitive Computation 4(4): 477-496.

[5] Cambria, E., Benson, T., Eckl, C. & Hussain, A. (2012). Sentic PROMs: Application of sentic computing to the development of a novel unified framework for measuring health- care quality, Expert Systems with Applications 39(12): 10533-10543.

[6] Cambria, E., Livingstone, A. & Hussain, A. (2012). The hourglass of emotions, in A. Esposito, A. Vinciarelli, R. Hoffmann & V. Muller (eds), Cognitive Behavioral Systems, Vol. 7403 of Lecture Notes in Computer Science, Springer, Berlin Heidelberg, pp. 144-157.

[7] Cambria, E., Olsher, D. & Kwok, K. (2012). Sentic activation: A two-level affective common sense reasoning framework, AAAI, Toronto, pp. 186-192.

[8] Cambria, E., Havasi, C. & Hussain, A. (2012). SenticNet 2: A semantic and affective resource for opinion mining and sentiment analysis, FLAIRS, Marco Island, pp. 202-207

[9] Cambria, E., Song, Y.,Wang, H. & Howard, N. (2013). Semantic multi-dimensional scaling for open-domain sentiment analysis, IEEE Intelligent Systems, doi: 10.1109/MIS.2012.118.

[10] SenticNet API -

[11] IEEE IS Special Issue on Concept-Level Opinion and Sentiment Analysis -


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.

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 mainly rely on parts of text in which opinions and sentiments 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.

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. In particular, sentic computing involves the use of AI and Semantic Web techniques, for knowledge representation and inference; mathematics, for carrying out tasks such as graph mining and multi-dimensionality reduction; linguistics, for discourse analysis and pragmatics; psychology, for cognitive and affective modeling; sociology, for understanding social network dynamics and social influence; finally ethics, for understanding related issues about the nature of mind and the creation of emotional machines.

Tutorial Program

• Introduction (5 mins)

• New Avenues in Sentiment Analysis Research
From Heuristics to Discourse Structure (5 mins)
From Coarse- to Fine-Grained Analysis (5 mins)
From Keywords to Concepts (15 mins)

• Sentic Computing Models
The Hourglass of Emotions (15 mins)
AffectiveSpace (15 mins)

• Sentic Computing Techniques
Sentic Medoids (10 mins)
Sentic Activation (10 mins)
Sentic Panalogy (10 mins)

• Sentic Computing Tools
SenticNet (15 mins)
IsaCore (15 mins)
Sentic Neurons (5 mins)

• Building a Sentic Engine
Sentic Parser (10 mins)
Sentic API (15 mins)
Application Samples (25 mins)

• Conclusion (5 mins)

Impact and Relevance

Evidence of the impact of sentic computing is found in the adoption of the approach by several leading American, British, and Asian companies, including: Zoral Inc., Luminoso Inc., Abies Ltd., Patient Opinion Ltd., Sitekit Solutions Ltd., HP Labs India, and Microsoft Research Asia.

For these reasons, sentic computing has also been recently put forward as best impact case study to the UK Research Excellence Framework (REF) by the University of Stirling.

The World Wide Web Conference is a global event bringing together key researchers, innovators, decision-makers, technologists, and business experts trying to make meaning out of Web data. Within this research and business area, opinion mining and sentiment analysis have become more and more important subtasks in recent years. However, there are still many challenges including social information understanding and integration.

Target Audience and Prerequisites

The target audience includes researchers and professionals in the fields of sentiment analysis, Web datamining, and related areas. The tutorial also aims to attract researchers from industry community as it covers research efforts for the development of applications in fields such as commerce, tourism, education, and health.

We expect the audience to have basic computer science skills, but psychologists and sociologists are also very welcome. Participants will learn not only state-of-the-art approaches to concept-level sentiment analysis, but also sentic computing techniques and tools to be used for practical opinion mining.


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