Conf: Tutorial, Sentic Computing (IEEE-SSCI-13, WWW-13, IADIS-ICWI-13), April 2013

Sat Feb 16 20:23:55 UTC 2013

Date: Fri, 15 Feb 2013 16:09:17 +0800
From: Erik Cambria <cambria at nus.edu.sg>
X-url: http://sentic.net/sentics

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.

AIMS AND SCOPE
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

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.

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

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

TARGET AUDIENCE AND PREREQUISITES 
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.

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