1. 06/05/2013
1
Data
mining
for
analyzing
the
social
media
Social
Networks
Video/picture
sharing
Opinions
News
websites
Blogs
Knowledge
sharing
Microblogging
eminar
at
4/18/2013
PresentaCon:
J.
Velcin
hGp://mediamining.univ-­‐lyon2.fr/people/velcin
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
Ecosystem
of
ERIC
Lab
2
Axe Carrés 2 ter
BSc
&
MSc
degrees
BI,
data
mining,
staCsCcs
2
teams:
SID
&
DMD
Academics
Companies
Context
The
big
picture
Online
discussions
½
-­‐sup.
clustering
ImagiWeb
Conclusion
Lyon
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
Research
landscape
3
Data
Data-­‐
warehouse
Knowledge
ETL
Online
analysis
Data
mining
D
e
c
i
s
i
o
n
Complex
data
integraCon
MulCdimensional
modeling
Context
The
big
picture
Online
discussions
½
-­‐sup.
clustering
ImagiWeb
Conclusion
Data
Mining
&
Decision
(DMD)
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
Data
Mining
&
Decision
(DMD)
4
Social
Networks
Microblogging
Video/picture
sharing
Opinion
sharing
News
websites
Blogs
Knowledge
sharing
e.g.
Social
Media
-­‐
heterogeneous
-­‐
voluminous
-­‐
interconnected
-­‐
evolving
RecommandaCon
Summzariz
aCon
InformaCon
retrieval
MulCcriteria
analysis
Machine
learning
Graph
analysis
Complex
data
analysis
Topological
learning
Text
mining
Prac<cal
issue
Approach
Goal:
coping
with
complex
data
Context
The
big
picture
Online
discussions
½
-­‐sup.
clustering
ImagiWeb
Conclusion

2. 06/05/2013
2
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
Outline
" The
big
picture
" Modeling
and
analyzing
online
discussions
" Semi-­‐supervised
clustering
" Focus
on
Project
ImagiWeb
" Future
lines
of
research
5
Context
The
big
picture
Online
discussions
½
-­‐sup.
clustering
ImagiWeb
Conclusion
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
Outline
" The
big
picture
" Modeling
and
analyzing
online
discussions
" Semi-­‐supervised
clustering
" Focus
on
Project
ImagiWeb
" Future
lines
of
research
6
Context
The
big
picture
Online
discussions
½
-­‐sup.
clustering
ImagiWeb
Conclusion
Section
1
The
big
picture
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
" A
long
questioning
" Social
representation
through
the
media
[Lippman,22]
[Moscovici,76]
[Newman
and
Block,06]
" Numeric
watch
on
the
Web
[Chateauraynaud,03]
8
Public
event
From
facts
to
people:
the
essential
role
of
media
Context
The
big
picture
Online
discussions
½
-­‐sup.
clustering
ImagiWeb
Conclusion

3. 06/05/2013
3
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
Information
overload
9
Image
credit:
Go-­‐Globe.com
Context
The
big
picture
Online
discussions
½
-­‐sup.
clustering
ImagiWeb
Conclusion
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
Data
journalism
10
" Crucial
need
to
catch
the
meaning
of
voluminous
data
provided
by
modern
social
media,
in
order
to
design
new
search
engine
systems
" In
particular
(MSND
workshop@WWW’12)
" “How
to
surface
the
best
comments,
videos
and
pictures
from
a
variety
of
sources
in
real
time
and
then
how
to
verify
them
?”
" “How
to
quickly
surface
the
best
comments
and
work
out
which
ones
are
worth
investigating
further
?”
" “How
to
identify
quickly
the
key
influencers
on
any
particular
story,
so
they
can
get
inside
information
or
interview
them
for
their
news
outlets
?”
Context
The
big
picture
Online
discussions
½
-­‐sup.
clustering
ImagiWeb
Conclusion
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
Salvaged
by
(media)
curation?
" Term
originated
from
Art,
appears
~2011
" Three-­‐step
process:
" Aggregation:
gathering
" Editorialize:
sorting,
categorizing,
summarizing,
presenting…
" Disseminate:
contextualizing,
sharing
" Important
role
of
the
curator
" Difference
between
“full
curation”
and
automatic
edition
(e.g.,
paper.li)
" Many
platforms
(Scoop.it!,
Storify,
Storiful,
Hopflow,
Stumbleupon,
Patch…):
http://socialcompare.com/fr/comparison/curation-­‐
platforms-­‐amplify-­‐knowledge-­‐plaza-­‐storify
11
[Rosenbaum,11]
Context
The
big
picture
Online
discussions
½
-­‐sup.
clustering
ImagiWeb
Conclusion
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
A
case
study:
the
“HuffPost”
12
" Linked
with
social
networks
" Topically
indexed
" Available
on
various
devices
" Commented
news
" Community
of
bloggers
" Journalist
can
play
both
the
roles
of
curator
and
community
manager
Context
The
big
picture
Online
discussions
½
-­‐sup.
clustering
ImagiWeb
Conclusion

4. 06/05/2013
4
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
Outline
" The
big
picture
" Modeling
and
analyzing
online
discussions
" Semi-­‐supervised
clustering
" Focus
on
Project
ImagiWeb
" Future
lines
of
research
13
Context
The
big
picture
Online
discussions
½
-­‐sup.
clustering
ImagiWeb
Conclusion
Section
2
Modeling
and
analyzing
online
discussions
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
Online
discussions
" Motivation:
" Numerous
available,
often
underused
data
" Crucial
to
feel
the
opinion
of
people
" Contributions:
" Recommending
key
messages
[Stavrianou
et
al.,09,10]
" Extracting
the
latent
social
network
[Forestier
et
al.,11]
" Detecting
celebrities
from
online
forums
[Forestier
et
al.,12]
" Surfacing
roles
with
unsupervised
mechanisms
[Anukhin
et
al.,12]
15
Context
The
big
picture
Online
discussions
½
-­‐sup.
clustering
ImagiWeb
Conclusion
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
16Julien Velcin - présentation ARC6 18 Octobre 2012

5. 06/05/2013
5
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
Anatomy
of
an
online
discussion
17
A
B
C
A
C
B
D D
A
B
C
Context
The
big
picture
Online
discussions
½
-­‐sup.
clustering
ImagiWeb
Conclusion
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
Recommending
key
messages
" “interesting”
message:
popular,
opinionated,
pioneer
etc.
" Formalization
of
6
criteria
+
simple
aggregation
" Comparison
to
manually-­‐labelled
data
on
8
french
forums
" Results
for
a
priori
evaluation:
" F1-­‐Measure
ranges
from
0.2
to
0.3
for
a
single
criterion
" F1-­‐Measure
equals
0.48
for
aggregated
criteria
(simple
mean)
" Results
for
a
posteriori
evaluations:
18
1
[Stavrianou
et
al.,09,10]
Context
The
big
picture
Online
discussions
½
-­‐sup.
clustering
ImagiWeb
Conclusion
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
Extracting
the
(latent)
social
network
" Latent SN = reply-to links + name citation + text quotation
" Name citation: bad spelling, compound names, abbreviations…
(what about “obama49”?)
" Our solution: edit distance, soundex, PoS to detect nouns
" Text quotation: cut-paste without quotation marks, rephrasing…
" Our solution: string matching, locality principle (comparing close
messages), use quotation marks if provided
19
2
[Forestier
et
al.,11]
Context
The
big
picture
Online
discussions
½
-­‐sup.
clustering
ImagiWeb
Conclusion
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
Detecting
celebrities
" Modeling the forum discussion with a graph G=(V,E)
" vertice v = forum participant
" edge e = link (implicit or explicit) between two participants
" Weighted in-degree of v: deg-(v)
" Weighted out-degree of v: deg+(v)
" p(v) = set of messages posted by v
" p~ = average of messages
" thr(v) = set of threads not initiated by v
20
3
[ForesCer
et
al.,12]
Context
The
big
picture
Online
discussions
½
-­‐sup.
clustering
ImagiWeb
Conclusion

6. 06/05/2013
6
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
Detecting
celebrities
" Extracting social roles from a SN is a key issue
[Fisher et al.,06] [Himelboim et al.,09] [Forestier et al.,12]
" Some examples of roles:
" Leader: very participative user, who initiates discussion threads and
makes the animation
" Expert: user particularly active in a restrictive number of topics
" Celebrity: public person well known by the participants
" Flammer: user with a negative behavior, who can generate conflicts
" Lurker: user who has a low participation in the discussion
" In the following, we have chosen to focus on the explicit “celebrity”
role within online discussion forums
21
3
Context
The
big
picture
Online
discussions
½
-­‐sup.
clustering
ImagiWeb
Conclusion
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
Detecting
celebrities
" Formalize the criteria given by [Golder and Donath,04]
22
3
Context
The
big
picture
Online
discussions
½
-­‐sup.
clustering
ImagiWeb
Conclusion
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
Detecting
celebrities
" Based on these atomic criteria, we define 3 meta-criteria:
" meta-criterion 1: all the basic criteria must be satisfied (necessary
conditions), and we rank the interesting users in descending order
relative to the total number of posts
" meta-criterion 2: id. but with a ranking depending on the user’s average
forum participation multiplied by the number of posts
" meta-criterion 3: id. but taking into account name citation and text
quotation
" Evaluation measure: compare the ranking of our meta-criteria with
the number of fans of each user (>800) = gold standard
" Dataset:
" 57 forums from the US version of the Huffington Post
" 3 topics: politics, media, living
" Overall 11,443 unique users and 35,175 posts
23
3
Context
The
big
picture
Online
discussions
½
-­‐sup.
clustering
ImagiWeb
Conclusion
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
24
[Forestier
et
al.,12]
Context
The
big
picture
Online
discussions
½
-­‐sup.
clustering
ImagiWeb
Conclusion

7. 06/05/2013
7
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
Surfacing
roles
" New collaboration between and
" Bottom-up “emerging” roles:
25
Axe Carrés 2 ter
4
Context
The
big
picture
Online
discussions
½
-­‐sup.
clustering
ImagiWeb
Conclusion
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
Surfacing
roles
" Discussions about 6 popular TV shows from TWOP forums
" Parent-child relationship is restored using “quote” mechanism:
" check previous 20 messages in the thread;
" a parent has to contain at least 95% of the quoted text.
26
4
Context
The
big
picture
Online
discussions
½
-­‐sup.
clustering
ImagiWeb
Conclusion
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
Surfacing
roles
" Profiling users using temporal-aware features:
" weighted in-degree,
" weighted out-degree,
" node in-g-index,
" node out-g-index,
" catalytic power,
" number of posts,
" cross-topic entropy.
" The role identification procedure is applied to the time series of
feature vectors of 1 263 forum users.
" Using moving time windows (size=1 week, shift=1 day)
27
4
Context
The
big
picture
Online
discussions
½
-­‐sup.
clustering
ImagiWeb
Conclusion
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
Surfacing
roles
" Clustering time series
" Basic k-means algorithm
" Hartigan’s index used for estimating the best k
28
[Anokhin
et
al.,12]
4
Context
The
big
picture
Online
discussions
½
-­‐sup.
clustering
ImagiWeb
Conclusion

8. 06/05/2013
8
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
Surfacing
roles
" Some
observations:
29
4
Context
The
big
picture
Online
discussions
½
-­‐sup.
clustering
ImagiWeb
Conclusion
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
Outline
" The
big
picture
" Modeling
and
analyzing
online
discussions
" Semi-­‐supervised
clustering
" Focus
on
Project
ImagiWeb
" Future
lines
of
research
30
Context
The
big
picture
Online
discussions
½
-­‐sup.
clustering
ImagiWeb
Conclusion
Section
4
Semi-­‐supervised
clustering
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
Temporal-­‐driven
clustering
" Goal:
detecting
typical
patterns
over
time
" How
to
deal
with
temporally
described
entities?
" Applications:
" Evolution
of
nation’s
political
states
(proof
of
concept)
" Trajectories
over
roles
" Evolution
of
entities’
images
(c.f.
ImagiWeb)
32
φ2
φ1
t1
t2
t3
t1
t2
t3
x1
d
x2
d
x3
d
x4
d
x5
d
x6
d
t2
t3
t1
Context
The
big
picture
Online
discussions
½
-­‐sup.
clustering
ImagiWeb
Conclusion

9. 06/05/2013
9
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
Temporal-­‐driven
clustering
" Detect
typical
evolution
patterns
of
individuals
in
the
dataset:
" phases
through
which
the
entity
collection
went
over
time
" trajectory
of
entities
through
the
different
phases
33
Context
The
big
picture
Online
discussions
½
-­‐sup.
clustering
ImagiWeb
Conclusion
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
Temporal-­‐aware
constrained
clustering
" The
resulted
partition
must
ensure:
" descriptive
coherence
of
clusters;
" temporal
coherence
of
clusters;
" continuous
segmentation
of
observations
belonging
to
an
entity
" Objective
function
to
minimize
(inspired
by
semi-­‐supervised
clustering
clustering
[Wagstaff
and
Cardie,00])
+
use
of
K-­‐Means-­‐like
algorithm:
34
Temporal-­‐aware
dissimilarity
measure
ConCguity
penalty
measure
(a)
(b)
(a)
(b)
Context
The
big
picture
Online
discussions
½
-­‐sup.
clustering
ImagiWeb
Conclusion
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
Experiments
on
political
dataset
" 23
countries,
60
years
" 207
political,
demographic,
social
and
economic
variables
" Running
TDCK-­‐Means
(8
clusters,
β
=
0.003
and
δ
=
3)
35
Context
The
big
picture
Online
discussions
½
-­‐sup.
clustering
ImagiWeb
Conclusion
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
Experiments
on
political
dataset
36
Context
The
big
picture
Online
discussions
½
-­‐sup.
clustering
ImagiWeb
Conclusion

10. 06/05/2013
10
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
Experiments
on
political
dataset
37
Context
The
big
picture
Online
discussions
½
-­‐sup.
clustering
ImagiWeb
Conclusion
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
Experiments
on
political
dataset
38
Context
The
big
picture
Online
discussions
½
-­‐sup.
clustering
ImagiWeb
Conclusion
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
Outline
" The
big
picture
" Modeling
and
analyzing
online
discussions
" Semi-­‐supervised
clustering
" Focus
on
Project
ImagiWeb
" Future
lines
of
research
39
Context
The
big
picture
Online
discussions
½
-­‐sup.
clustering
ImagiWeb
Conclusion
Section
5
Focus
on
Project
ImagiWeb
hGp://eric.univ-­‐lyon2.fr/~jvelcin/imagiweb

11. 06/05/2013
11
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
Project
ImagiWeb
" Goal
of
Project
ANR
ImagiWeb:
analyzing
the
life
cycle
(production,
diffusion,
evolution)
of
images
through
the
Web
2.0
" Strong
points:
" Joint
analysis
of
opinions,
topics,
social
networks…
" Involvement
of
(true)
researchers
in
LLSSH
" Partners:
" ERIC:
data
mining,
machine
learning
" LIA:
text/opinion
mining,
information
retrieval
" CEPEL:
social
scientists,
specialist
in
politics
study
" XRCE:
information
extraction,
NLP
" AMI
Soft.:
numeric
watch
" EDF
R&D:
end-­‐user,
semiology
study
41
Context
The
big
picture
Online
discussions
½
-­‐sup.
clustering
ImagiWeb
Conclusion
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
Project
ImagiWeb
42
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(5+8)'
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Context
The
big
picture
Online
discussions
½
-­‐sup.
clustering
ImagiWeb
Conclusion
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
Platform
for
performing
the
annotation
" Web
applications
designed
for
annotating
~10k
tweets
+
200
blog
comments;
22
annotators
are
working
on
it
right
now!
" Output:
(mφ
;
mt;
mp
;
ma
;
mt
;
ms
)
43
Context
The
big
picture
Online
discussions
½
-­‐sup.
clustering
ImagiWeb
Conclusion
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
Platform
for
performing
the
annotation
" Web
applications
designed
for
annotating
~10k
tweets
+
200
blog
comments;
22
annotators
are
working
on
it
right
now!
" Output:
(mφ
;
mt;
mp
;
ma
;
mt
;
ms
)
44
Context
The
big
picture
Online
discussions
½
-­‐sup.
clustering
ImagiWeb
Conclusion

12. 06/05/2013
12
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
Catching
image’s
evolution
over
time
" Input:
set
of
tuples
(mφ
;
mt;
mp
;
ma
;
mt
;
ms
)
" Some
good
questions:
" What
is
an
image?
" How
to
sum
up
the
bunch
of
(temporally-­‐situated
and
spatially-­‐located)
opinions?
" First
insight:
investigating
time
series
analysis,
temporally-­‐driven
clustering,
graphical
models…
" Fortunately
we’ll
have
a
fulltime
post-­‐doc
student
to
work
on
it!
45
Context
The
big
picture
Online
discussions
½
-­‐sup.
clustering
ImagiWeb
Conclusion
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
Recent
work
on
opinion
mining
" Participation
to
Sem-­‐Eval
2013
" Task
2.B:
Discriminating
positive
(+)
from
negative
(-­‐)
opinions
(+
neutral)
" Very
recent
work:
improving
basic
NB
by
using
background
knowledge
(seed
lists)
" 6/35
and
3/16
on
the
official
tweet
dataset!
" Results
on
our
own
datasets:
46
[paper
just
submiGed]
Context
The
big
picture
Online
discussions
½
-­‐sup.
clustering
ImagiWeb
Conclusion
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
Outline
" The
big
picture
" Modeling
and
analyzing
online
discussions
" Semi-­‐supervised
clustering
" Focus
on
Project
ImagiWeb
" Future
lines
of
research
47
Context
The
big
picture
Online
discussions
Topics
Clustering
ImagiWeb
Conclusion
Section
6
Future
lines
of
research

13. 06/05/2013
13
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
An
integrated
view
Research
+
tools
+
applications
" Ongoing
Research
" Structured
temporal-­‐driven
clustering
(M.
A.
Rizoiu,
PhD
student)
" Bridging
the
gap
between
topics
and
concepts
(M.
A.
Rizoiu,
PhD
student)
" Multi-­‐document
summarization
of
online
discussions
(C.
Cercel,
PhD
student,
in
collaboration
with
the
Polytechnic
Institute
of
Bucharest)
" Bottom-­‐up,
dynamic
extraction
of
roles
(A.
Lumbreras,
PhD
students,
in
collaboration
with
Technicolor)
" Dynamic
joint
extraction
of
topics
and
opinions
(M.
Dermouche,
PhD
student,
in
collaboration
with
AMI
Software)
" Extracting
opinionated
images
from
tweets
and
blogs
in
an
unsupervised
way
(Y.
Kim,
post-­‐doc
student,
in
collaboration
with
LIA)
49
Context
The
big
picture
Online
discussions
Topics
Clustering
ImagiWeb
Conclusion
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
An
integrated
view
" Tools
" MediaMining:
a
full
open-­‐access
platform
for
analyzing
online
discussions
" Applications
" Reputation
Management
services
=>
Project
ImagiWeb,
with
specialist
in
political
studies
(2012-­‐2015,
~860k)
" Discourse
analysis
in
public
opinion
=>
Project
DANuM,
with
linguists
(2013-­‐2014,
23k)
=>
Project
ALICE,
with
social
scientists
and
specialists
in
communication
(just-­‐submitted)
" The
next
step:
datamining-­‐based
services
for
“curation
support”,
with
specialist
in
communication
and
journalists
50
Context
The
big
picture
Online
discussions
Topics
Clustering
ImagiWeb
Conclusion
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
Focus
on
the
collaboration
DAL/Lyon
" 3
possible
scientific
contributions:
" Labeling
hierarchical
topic
models
" Labeling
dynamic
topic
models
" Visualization
of
hierarchical/dynamic
topic
models
51
ArCficial
Neuronal
Network
Neuroscience
OpCmizaCon
Efficiency
(staCsCcs)
Learning
theory
Vision
chip
GeneraCve
model
Graphical
models
Neural
networks
Background
Computer
vision
Markov
decision
process
ComputaCon
al
complexity
theory
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
References
(excerpt)
" Anokhin
N.,
J.
Lanagan,
J.
Velcin
(2012),
Social
Citation:
Finding
Roles
in
Social
Networks.
An
Analysis
of
TV-­‐Series
Web
Forums.
Second
International
Workshop
on
Mining
Communities
and
People
Recommenders
(COMMPER),
in
conjunction
with
ECML/PKDD,
Bristol,
UK.
" Dermouche
M.,
J.
Velcin,
S.
Loudcher,
L.
Khouas
(2013),
Une
nouvelle
mesure
pour
l'évaluation
des
méthodes
d'extraction
de
thématiques
:
la
Vraisemblance
Généralisée.
Actes
de
la
13ème
Conférence
Francophone
sur
l'Extraction
et
la
Gestion
des
Connaissances
(EGC).
Toulouse,
France.
" Forestier,
M.,
Stavrianou,
A.,
Velcin,
J.
and
Zighed,
D.A.
(2012),
Roles
in
Social
Networks:
Methodologies
and
Research
Issues.
Web
Intelligence
and
Agent
Systems:
An
International
Journal
(WIAS).
" Musat,
C.,
Velcin,
J.,
Rizoiu,
M.A.
and
Trausan-­‐Matu,
S.
(2011),
Improving
Topic
Evaluation
Using
Conceptual
Knowledge.
Proceedings
of
the
22nd
International
Joint
Conference
on
Artificial
Intelligence
(IJCAI).
Barcelona,
Spain.
" Rizoiu
M.A.,
J.
Velcin,
S.
Lallich
(2012),
Structuring
typical
evolutions
using
Temporal-­‐Driven
Constrained
Clustering.
Proceedings
of
the
24th
IEEE
Internatinal
Conference
on
Tools
with
Artificial
Intelligence
(ICTAI).
Athens,
Greece.
Best
student
paper
award.
" Stavrianou,
A.,
Velcin,
J.
and
Chauchat,
J.H.
(2009),
A
combination
of
opinion
mining
and
social
network
techniques
for
discussion
analysis.
Revue
des
Nouvelles
Technologies
de
l'Information
(RNTI),
Cepadues.
52
Context
The
big
picture
Online
discussions
Topics
Clustering
ImagiWeb
Conclusion

14. 06/05/2013
14
eminar
at
housie
University
–
4/18/2013
–
ulien
elcin
Thank
you!
53
Context
The
big
picture
Online
discussions
Topics
Clustering
ImagiWeb
Conclusion