Figure 4: Sentiment analysis.
CLOSING THOUGHTS
Trending topics represent the popular
“topics of conversation,” and when detected in real time, these hot topics are
the social pulses that are usually ahead
of any standard news media. Data analyzed via managed data centers can provide key insights into the evolving nature
and patterns of social information and
opinion and the general sentiment prevailing over such subjects.
Aveek Mukhopadhyay is an associate manager
at Mu Sigma where he works with the Innovation
& Development Team with a core focus on driving
the adoption of advanced analytical platforms
and techniques both internally and externally. He
has interests in the fields of text mining, machine
learning and analytics automation.
Roger Barga, Ph.D., is group program manager
for the CloudML team at Microsoft Corporation
where his team is building machine learning as
A NA L Y T I C S
a service in the cloud. Barga is also a lecturer
in the Data Science program at the University
of Washington. He joined Microsoft in 1997 as a
researcher in the Database Group of Microsoft
Research (MSR), where he was involved in a
number of systems research projects and product
incubation efforts, before joining the Cloud and
Enterprise Division of Microsoft in 2011.
NOTES & REFERENCES
1. The Economist (Feb. 25, 2010), “The Data Deluge”
(http://www.economist.com/node/15579717).
2. David M. Blei, “Probabilistic Topic Models,”
Communications of the ACM, April 2012, Vol. 55, No.
4 (http://www.cs.princeton.edu/~blei/papers/Blei2012.
pdf).
3. Xiaowen Ding, Bing Liu and Philip S. Yu,
“A Holistic Lexicon-Based Approach to Opinion
Mining” (http://www.cs.uic.edu/~liub/FBS/opinionmining-final-WSDM.pdf).
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