TUTORIAL: Sentic Computing (IEEE-SSCI-13, WWW-13, IADIS-ICWI-13)

Apologies for cross-posting.

A tutorial on sentic computing will be delivered at the IEEE International Symposium on Intelligent Agents (this April in Singapore), the World Wide Web conference (this May in Rio De Janeiro, Brazil), and the IADIS International Conference WWW/INTERNET (this October in Fort Worth, Texas). For more information, please visit the respective conference websites.

The focus of the 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 — http://sentic.net/sentics

[2] SenticNet resources — http://sentic.net/downloads

[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 — http://sentic.net/api

[11] IEEE IS Special Issue on Concept-Level Opinion and Sentiment Analysis — http://computer.org/intelligent/cfp2

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.


I) Introduction

II) New Avenues in Sentiment Analysis Research
- From Heuristics to Discourse Structure
- From Coarse- to Fine-Grained Analysis
- From Keywords to Concepts

III) Sentic Computing Models
- The Hourglass of Emotions
- AffectiveSpace

IV) Sentic Computing Techniques
- Sentic Medoids
- Sentic Activation
- Sentic Panalogy

V) Sentic Computing Tools
- SenticNet
- IsaCore
- Sentic Neurons

VI) Building a Sentic Engine
- Sentic Parser
- Sentic API
- Application Samples

VII) Conclusion

The target audience includes researchers and professionals in the fields of sentiment analysis, Web data mining, 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.

Best Regards,
Erik Cambria

PS: if you are attending WWW13, please also consider submitting to MABSDA (http://sentic.net/mabsda) by 28th February.
Erik Cambria, PhD
Research Scientist

Temasek Laboratories
Cognitive Science Programme
National University of Singapore
28 Medical Drive, 117456, Singapore

Web: http://sentic.net

Email: cambria@nus.edu.sg
Twitter: http://twitter.com/senticnet

Facebook: http://facebook.com/senticnet

Received on Friday, 15 February 2013 08:09:54 UTC