Conf: WWW'14, Tutorial on Concept-Level Sentiment Analysis, Seoul, Korea

Sat Feb 8 11:16:21 UTC 2014

Date: Fri, 7 Feb 2014 17:43:30 +0800
From: Erik Cambria <cambria at nus.edu.sg>
Message-ID: <6BBCDA75-D5AB-4402-BC54-3E4965361F62 at nus.edu.sg>

Apologies for cross-posting, 

Participants are invited to the WWW’14 tutorial on Concept-Level
Sentiment Analysis, which will be held within the World Wide Web
conference this April in Seoul, Korea. The tutorial aims to provide its
participants means to efficiently design models, techniques, tools, and
services for concept-level sentiment analysis and their commercial
realizations. The tutorial draws on insights resulting from the recent
IEEE Intelligent Systems special issues on Concept-Level Opinion and
Sentiment Analysis and the IEEE CIM special issue on Computational
Intelligence for Natural Language Processing. The tutorial includes a
hands-on session to illustrate how to build a concept-level
opinion-mining engine step-by-step, from semantic parsing to
concept-level reasoning.

BACKGROUND AND MOTIVATIONS

As the Web rapidly evolves, Web users are evolving with it. In an 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.

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.

Concept-level sentiment analysis focuses on a semantic analysis of text
through the use of web ontologies or semantic networks, which allow the
aggregation of conceptual and affective information associated with
natural language opinions. By relying on external knowledge, such
approaches step away from blind use of keywords and word co-occurrence
count, but rather rely on the implicit features associated with natural
language concepts. Unlike purely syntactical techniques, concept-based
approaches are able to detect also sentiments that are expressed in a
subtle manner, e.g., through the analysis of concepts that do not
explicitly convey any emotion, but which are implicitly linked to other
concepts that do so. The bag-of-concepts model can represent semantics
associated with natural language much better than bags-of-words. In the
bag-of-words model, in fact, a concept such as cloud computing would be
split into two separate words, disrupting the semantics of the input
sentence (in which, for example, the word cloud could wrongly activate
concepts related to weather).

The analysis at concept-level allows for the inference of semantic and
affective information associated with natural language text and, hence,
enables comparative fine-grained feature-based sentiment
analysis. Rather than gathering isolated opinions about a whole item
(e.g., iPhone5), users are generally more interested in comparing
different products according to specific features (e.g., iPhone5’s vs
Galaxy S3’s touchscreen), or even sub-features (e.g., fragility of
iPhone5’s vs Galaxy S3’s touchscreen). In this context, the construction
of comprehensive common and common-sense knowledge bases is key for
feature-spotting and polarity detection, respectively. Common-sense, in
particular, is necessary to properly deconstruct natural language text
into sentiments – for example, to appraise the concept small room as
negative for a hotel review and small queue as positive for a post
office, or the concept go read the book as positive for a book review
but negative for a movie review.

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 (10 mins)

- Concept-Level Models
	- Knowledge acquisition models (10 mins)
	- Emotion categorization models (10 mins)
	- Vector space models (10 mins)

- Concept-Level Techniques
	- Analogical reasoning (10 mins)
	- Parallel analogy (10 mins)
	- Spreading activation (10 mins)

- Concept-Level Tools
	- Sentiment resources (15 mins)
	- Common knowledge repositories (15 mins)
	- Aspect mining and polarity detection (10 mins)

- Building a Concept-Level Opinion-Mining Engine
	- Semantic parsing (15 mins)
	- Sentic API (15 mins)
	- Application Samples (20 mins)

- Conclusion (5 mins)

IMPACT AND RELEVANCE

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
increasingly important subtasks in recent years. However, there are
still many challenges, including social information understanding and
integration, that need to be addressed. For these reasons, a tutorial on
concept-level sentiment analysis is strongly relevant to WWW’14.

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. The audience is expected to
have basic computer science skills, but psychologists and sociologists
are also very welcome. The tutorial not only covers state-of-the-art
approaches to concept-level sentiment analysis, but also provides
information about techniques and tools to be used for practical opinion
mining.

ABOUT THE TUTOR

Erik Cambria received his BEng and MEng with honors 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 a MINDEF-funded project on
Commonsense Knowledge Representation & Reasoning at the National
University of Singapore (Temasek Laboratories) 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 modeling,
intention awareness, HCI, and e-health. Erik is also chair of several
international conferences, e.g., Extreme Learning Machines (ELM), and
workshop series, e.g., ICDM SENTIRE. He is on the editorial board of
Springer Cognitive Computation and he is a guest editor of many other
leading AI journals. Erik is also a fellow of the Brain Sciences
Foundation, the Chinese Academy of Sciences, National Taiwan University,
Microsoft Research Asia, and HP Labs India.

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