Computational Intelligence - August 2016 - 21
As an example, we considered two results at different level of
the hierarchical co-clustering. In the former, considering the first
hierarchical level, one of the row co-cluster contains 164 Facebook posts that are associated to a cluster of 72 hashtags. As can
be seen in Fig. 3(a), they are mostly related to discussions about
the highly debated economic reforms and school. The second
example (see Fig. 3(b)) is extracted from the third level of the
hierarchical clustering and associates a set of 49 Facebook posts
to the reported set of hashtags: as can be seen, the set of terms
depict the discussion, mostly debated by the center-right parties,
around immigration and terrorism.
VIII. Conclusions and Future Work
In this paper, we proposed an integration framework in which
TV users' activities on different social media are collectively
represented, and possibly enriched with external knowledge,
such as information extracted from the EPGs, or available
ontological domain knowledge. We also discussed different
types of analysis that the integration data model enables.
Many research problems remain open. As future work, we
will address the scalability issues which immediately emerge
when engineering an industrial system based on the presented
framework. The intense activity of social media users turns into
the high dynamicity of the knowledge graph. Hence, we are
studying incremental (possibly approximate) versions of the
algorithms computing graph based popularity measures.
The data model allows us to track the temporal evolution of
users' activities. Thus, another future work includes the application of innovative algorithms and techniques to analyze time
series extracted from the graph.This will allow us to capture and
study how social phenomena (popularities, users' interests, and
communities of users sharing common interests) evolve in time.
Finally, for future work, we plan to leverage the most recent
research results in social media analytics and sentiment analysis to
further improve our framework. In particular, we will adopt some
event detection techniques (such as the one presented in [37]) to
support the automatic detection of emerging topics in social
media and we will consider sentic computing [38], [39] and AffectiveSpace [40], to bring sentiment analysis up to concept-level.
Acknowledgments
We are grateful to Alessio Antonini, Roberto Del Pero and
Fulvio Negro for their constructive discussions during the formalization of the integration framework.
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AUGUST 2016 | IEEE Computational intelligence magazine
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