@techreport{TD:100344,
	att_abstract={{This chapter will give an overview of recent media mining technologies and their applica-
tions to TV-related services. In particular, we will cover three main themes related to Social
TV. First, we will discuss retrieving social media content related to TV program. Some TV
shows such as "Lost" and "Heroes" have titles that are also common terms. Using these
titles as keywords to search for relevant messages will lead to low precision. Conversely,
many shows have long titles, which results in low recall as users don't use the full name
of the shows. Appropriately retrieved social media content can be used to display social
discussions on certain TV shows. Furthermore, retrieving such data with high precision and
recall is crucial to a wide spectrum of applications and data mining algorithms.
The second theme is social media mining for TV services. We will focus on sentiment
analysis and detection of in
uential users in social networks. Analyzing messages for sen-
timent can help uncover the aggregate opinion of a large number of users with respect to
certain TV shows or episodes. It can also be useful in presenting opposing views on the same
subject. Graph analysis can be used to determine the most in
uential users which discuss
certain TV programs.
The third theme is integrating the information mined from social media into Social TV
interfaces. This section describes the technologies that enable a user to interact with social
media through television sets. We will present VoiSTV, a voice-enabled social TV prototype
system created at AT&T Labs Research. We will also discuss previous work in this area, as
well as trends in the industry.}},
	att_authors={jf6737, br2187},
	att_categories={},
	att_copyright={{CRC Press, Taylor Francis LLC }},
	att_copyright_notice={{}},
	att_donotupload={},
	att_private={false},
	att_projects={},
	att_tags={},
	att_techdoc={true},
	att_techdoc_key={TD:100344},
	att_url={http://web1.research.att.com:81/techdocs_downloads/TD:100344_DS1_2011-01-12T20:00:07.882Z.pdf},
	author={Junlan Feng and Bernard Renger and Ovidiu Dan},
	institution={{TV Content Analysis}},
	month={February},
	title={{Enhancing Social TV through Social Media Mining}},
	year=2012,
}