Data mining for analyzing social media
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Data mining for analyzing social media Presentation Transcript

  • 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 !"#$%&' ("$)*"+,$)&' )-.'/')"$*)0*1&)&2' 3455)&'0461#7,)&' (5+8)' %51&)' (5+8)' 0)*9,)' (5+8)' 0)*9,)' (5+8)' 0)*9,)' :455)"$+1*)&' ;%<1+&'<)' =455,"1=+#4"' >&1$)&'?)@2'06+7,)A)2')$=.B' C"+6D&)'<)&'<4""%)&' <E)-0*)&&14"' C"+6D&)'<)&' 040,6+#4"&' F))<@+=G' (;CH(I!J' %5)A),*&' %5)A),*&' *%=)0$),*&' *%=)0$),*&' *%=)0$),*&' 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