Computational Intelligence - August 2016 - 8
Guest
Editorial
Erik Cambria
School of Computer Science and Engineering,
Nanyang Technological University, Singapore
Newton Howard
University of Oxford, UK
Yunqing Xia
Microsoft Research Asia, China
Tat-Seng Chua
National University of Singapore, Singapore
Computational Intelligence for Big Social Data Analysis
I
n the eras of social connectedness
and social colonization, people are
becoming increasingly enthusiastic
about interacting, sharing, and collaborating through 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
understandable to machines.
Big social data analysis grows out of
this need and combines multiple disciplines such as social network analysis,
multimedia management, social media
analytics, trend discovery, and opinion
mining. For example, studying the evolution of a social network merely as a
graph is very limited as it does not take
into account the information flowing
between network nodes. Similarly,
-processing social interaction contents
between network members without taking into account connections between
them is limited by the fact that information flows cannot be properly weighted.
Big social data analysis, instead, aims to
study large-scale Web phenomena such
as social networks from a holistic point
of view, i.e., by concurrently taking into
Digital Object Identifier 10.1109/MCI.2016.2572481
Date of publication: 18 July 2016
8
account all the socio-technical aspects
involved in their dynamic evolution.
Hence, big social data analysis is inherently interdisciplinary and spans areas
such as machine learning, graph mining,
information retrieval, knowledge-based
systems, linguistics, common-sense reasoning, natural language processing, and
big data computing.
Big social data analysis finds applications in several different scenarios. There
is a good number of companies, large
and small, that include the analysis of
social data as part of their missions. Big
social data analysis can be exploited for
the creation and automated upkeep of
review and opinion aggregation websites, in which opinionated text and videos are continuously gathered from the
Web and not restricted only to product
reviews, but also to wider topics such as
political issues and brand perception. Big
social data analysis has also a great
potential as a sub-component technology for other systems. They can enhance
the capabilities of customer relationship
management and recommendation systems allowing, for example, to find out
which features customers are particularly
happy about or to exclude the recommendations items that have received
very negative feedbacks. Similarly, they
can be exploited for affective tutoring
and affective entertainment or for troll
filtering and spam detection in online
social communication.
Business intelligence is also one of
the main factors behind corporate interest in big social data analysis. Nowadays,
companies invest an increasing amount
IEEE Computational intelligence magazine | August 2016
of money in marketing strategies and
they are constantly interested in both
collecting and predicting the attitudes of
the general public towards their products and brands. The design of automatic
tools capable to mine sentiments over
the Web in real-time and to create condensed versions of them represents one
of the most active research and development areas. The development of such
systems, moreover, is not only important
for commercial purposes, but also for
government intelligence applications
able to monitor increases in hostile
communications or to model cyberissue diffusion.
Several commercial and academic
tools track public viewpoints on a largescale by offering graphical summarizations of trends and opinions in the
blogosphere. Nevertheless, most commercial off-the-shelf (COTS) tools are
limited to a polarity evaluation or a
mood classification according to a very
limited set of emotions. In addition,
such methods mainly rely on parts of
text in which emotional states are
explicitly expressed and, hence, they are
unable to capture opinions and sentiments that are expressed implicitly.
Because they are mainly based on statistical properties associated with words, in
fact, many COTS tools are easily tricked
by linguistic operators such as negation
and disjunction.
The main motivation for this Special
Issue is to explore how computational
intelligence can help overcome such
hurdles through new forms of processing that can handle high volume, high
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