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- 1. Towards building effec2ve computa2onal sociopragma2cs models of human cogni2on Mona Diab George Washington University
- 2. Acknowledgement • Many collaborators: Dragomir Radev, Amjad Abu Jbara, Pradeep Dasigi, Weiwei Guo, Owen Rambow, Julia Hirschberg, Kathy Mckeown, Mustafa Mughazy, Heba Elfardy, Vinod Prabhakaran, Greg Werner, Muhammad Abdulmageed • Research supported by IARPA SCIL program and DARPA DEFT & BOLT programs and Google Faculty award • Slides adapted from several publica2on presenta2ons
- 3. What is sociopragma2cs? • The aspect of language use that relates to everyday social prac.ces. hVp://www.wordsense.eu dic2onary – What are social prac2ces? Well … from our language focused prism J • Interac2ons, expressions of emo2ons/beliefs/opinions, etc.
- 4. Text and Social Rela2ons We can use linguis2c analysis techniques to understand the implicit rela2ons that develop in on-‐line communi2es Image source: clair.si.umich.edu
- 5. Overarching Agenda • Goal: AVempt to mine social media text for clues and cues on understanding human interac2ons • How: Iden2fy interes2ng sociolinguis2c behaviors and correlate them with linguis2c usage that are quan2fiable devices and build effec2ve models in the process • Compare these devices cross linguis2cally
- 6. Many Different Forms of Social Media • Communica2on • Collabora2on • Mul2media • Reviews & opinions
- 7. Social Media Explosion source: www.internetworldstats.com >3 billion Internet users worldwide >42.3% popula2on penetra2on (>48% in the MENA region) 75% of them used “Social Media”
- 8. Text in Social Media Some social media applica2ons are all about text
- 9. Text in Social Media Even the ones based on photos, videos, etc. generate a lot of discussions
- 10. Text in Social Media Huge amount of text exchanged in discussions
- 11. Do you s2ll need convincing that text is important! Yeah I thought not! Just checking J
- 12. Interes2ng Sociolinguis2c Phenomena: Social Constructs Mul2ple Viewpoints (Subgroups) Influencers Pursuit of Power Disputed Topics
- 13. Approach to processing social construct phenomena (Direc.ve from the IARPA SCIL Program) • Iden2fy language uses (LU) per2nent to the different social constructs (SC) • Correlate the LUs with Linguis2c Construc2ons/ Cons2tuents (LC)
- 14. Social Construct: Influencer (inf) • Language Uses – AVempt to Persuade – Agreement/Disagreement – Level of CommiVed Belief Influencers
- 15. Social Construct: Pursuit of Power (PoP) • Language Uses – AVempt to Persuade – Agreement/Disagreement – Level of CommiVed Belief – Nega2ve/Posi2ve Aktude – Who is talking about whom – Dialog PaVerns (non linguis2c) Pursuit of Power
- 16. Social Construct: Subgroup (Sub) • Language Uses – Agreement/Disagreement – Nega2ve/Posi2ve Aktude – Sarcasm – Level of CommiVed Belief – Signed Network (non linguis2c) Mul2ple Viewpoints (Subgroups)
- 17. LUs in our approach • AVempt to Persuade (Inf, PoP) • Agreement/Disagreement (Inf, PoP, Sub) • Level of CommiVed Belief (Inf, PoP) • Nega2ve/posi2ve aktude (Sub, PoP) • Sarcasm (Sub) • Who is talking about whom (PoP) • Dialog PaVerns (PoP) • Signed Network (Sub) Do not depend on linguis6c analysis Rely on linguis6c analysis
- 18. Cross language comparison: Generaliza2ons • In general similar LU level devices cross linguis2cally • AVempt to persuade – Claim: grounding in experience, commonly respected sources – Argumenta2on: evidence and support from other discussants • Agreement/Disagreement – Shared opinion (explicit expression), shared perspec2ve (implicit aktude) • Level of CommiVed Belief – CommiVed: The sun will rise tomorrow – Non commiVed: John may believe that the moon is made of cheese
- 19. Generaliza2ons • -‐ve/+ve aktude – Nega2ve language – Sen2ment/word polarity • Who is talking about whom – Use of men2ons and their frequency
- 20. But how do they differ in their linguis2c expression? • Arabic vs. English social media use different linguis2c cons2tuents (LC) to exhibit language use
- 21. Focus of this talk Influencers Pursuit of Power Disputed Topics Mul2ple Viewpoints (Subgroups)
- 22. Subgroup Detec2on Problem Discussion Thread Subgroups Discussant
- 23. Example The new immigra2on law is good. Illegal immigra2on is bad. Peter I totally disagree with you. This law is blatant racism. Mary Have you read all what Peter wrote? He is correct. Illegal immigra2on is bad and must be stopped. John You are clueless, Peter. Stop suppor2ng racism. Alexander Peter John Support the new law Against the new law Mary Alexander
- 24. Sample thread
- 25. Subgroup Detec2on System Overview Discussion Thread Subgroups Discussant Opinion Expressions Iden2fica2on Thread Parsing … disagree…… …....... ………… like…………… ………………… bad……. Candidate Target Iden2fica2on ..........you……... ......................... ......conserva1ves ideologues………. ……………………… ....…..Immigra1on law………………… Opinion-‐Target Pairing disagree You like Conserva2ve Ideologues bad Immigra2on law Reply Structure Candidate Target Iden2fica2on Clustering Discussant A9tude Profiles (DAPs)
- 26. Subgroup Detec2on System Overview Discussion Thread Subgroups Discussant Opinion Expressions Iden2fica2on Thread Parsing … disagree…… …....... ………… like…………… ………………… bad……. Candidate Target Iden2fica2on ..........you……... ......................... ......conserva1ves ideologues………. ……………………… ....…..Immigra1on law………………… Opinion-‐Target Pairing disagree You like Conserva2ve Ideologues bad Immigra2on law Reply Structure Candidate Target Iden2fica2on Clustering Discussant A9tude Profiles (DAPs)
- 27. 1 -‐ Thread Parsing The new immigra2on law is good. Illegal immigra2on is bad. Peter I totally disagree with you. This law is blatant racism. Mary Have you read all what Peter wrote? He is correct. Illegal immigra2on is bad and must be stopped. John You are clueless, Peter. Stop suppor2ng racism. Alexander P1 P2 P3 P4 D1 D2 D3 D4 Iden2fy Posts, Discussants, and the reply structure of the discussion thread
- 28. Subgroup Detec2on System Overview Discussion Thread Subgroups Discussant Opinion Expressions Iden2fica2on Thread Parsing … disagree…… …....... ………… like…………… ………………… bad……. Candidate Target Iden2fica2on ..........you……... ......................... ......conserva1ves ideologues………. ……………………… ....…..Immigra1on law………………… Opinion-‐Target Pairing disagree You like Conserva2ve Ideologues bad Immigra2on law Reply Structure Candidate Target Iden2fica2on Clustering Discussant A9tude Profiles (DAPs)
- 29. 2 -‐ Iden2fy Opinion Words* The new immigra2on law is good+. Illegal immigra2on is bad-‐. Peter I totally disagree-‐ with you. This law is blatant-‐ racism-‐. Mary Have you read all what Peter wrote? He is correct+. Illegal immigra2on is bad-‐ and must be stopped. John You are clueless-‐, Peter. Stop suppor2ng racism. Alexander P1 P2 P3 P4 D1 D2 D3 D4 *Iden2fying opinion words using Opinion Finder with an extended lexicon (implemented using random walks – Hassan & Radev, 2011)
- 30. Subgroup Detec2on System Overview Discussion Thread Subgroups Discussant Opinion Expressions Iden2fica2on Thread Parsing … disagree…… …....... ………… like…………… ………………… bad……. Candidate Target Iden2fica2on ..........you……... ......................... ......conserva1ves ideologues………. ……………………… ....…..Immigra1on law………………… Opinion-‐Target Pairing disagree You like Conserva2ve Ideologues bad Immigra2on law Reply Structure Candidate Target Iden2fica2on Clustering Discussant A9tude Profiles (DAPs)
- 31. 3-‐ Iden2fy Candidate Targets of Opinion Target Discussant ( e.g. you, Peter)` Topic/En1ty (e.g. The new immigra2on Law, Illegal Immigra2on)
- 32. Candidate Targets 3-‐ Iden2fy Candidate Targets of Opinion The new immigra2on law is good+. Illegal immigra2on is bad-‐. Peter I totally disagree-‐ with you. This law is blatant-‐ racism-‐. Mary Have you read all what Peter wrote? He is correct+. Illegal immigra2on is bad-‐ and must be stopped. John You are clueless-‐, Peter. Stop suppor2ng racism. Alexander P1 P2 P3 P4 D1 D2 D3 D4 All discussants are candidate Targets
- 33. Candidate Targets 3-‐ Iden2fy Candidate Targets of Opinion The new immigra2on law is good+. Illegal immigra2on is bad-‐. Peter I totally disagree-‐ with you. This law is blatant-‐ racism-‐. Mary Have you read all what Peter wrote? He is correct +. Illegal immigra2on is bad-‐ and must be stopped. John You are clueless-‐, Peter. Stop suppor2ng racism. Alexander P1 P2 P3 P4 D1 D2 D3 D4 D1 D1 D1 Iden2fy discussant men2ons (2pp or name) in the discussion D2
- 34. Candidate Targets 3-‐ Iden2fy Candidate Targets of Opinion The new immigra2on law is good+. Illegal immigra2on is bad-‐. Peter I totally disagree-‐ with you. This law is blatant-‐ racism-‐. Mary Have you read all what Peter wrote? He is correct +. Illegal immigra2on is bad-‐ and must be stopped. John You are clueless-‐, Peter. Stop suppor2ng racism. Alexander P1 P2 P3 P4 D1 D2 D3 D4 D1 D1 D1 D1 Peter Iden2fy anaphoric men2ons of discussants D2
- 35. Candidate Targets 3-‐ Iden2fy Candidate Targets of Opinion The new immigra1on law is good+. Illegal immigra1on is bad-‐. Peter I totally disagree-‐ with you. This law is blatant-‐ racism-‐. Mary Have you read all what Peter wrote? He is correct +. Illegal immigra1on is bad-‐ and must be stopped. John You are clueless-‐, Peter. Stop suppor2ng racism. Alexander P1 P2 P3 P4 D1 D2 D3 D4 D1 D1 D1 D1 Peter Topic1 Topic1 Topic2 Topic2 D2 Topic 1 Topic 2
- 36. 3-‐ Iden2fy Candidate Targets of Opinion • Techniques used to iden2fy topical targets – Named En2ty Recogni2on – Noun phrase chunking
- 37. Subgroup Detec2on System Overview Discussion Thread Subgroups Discussant Opinion Expressions Iden2fica2on Thread Parsing … disagree…… …....... ………… like…………… ………………… bad……. Candidate Target Iden2fica2on ..........you……... ......................... ......conserva1ves ideologues………. ……………………… ....…..Immigra1on law………………… Opinion-‐Target Pairing disagree You like Conserva2ve Ideologues bad Immigra2on law Reply Structure Candidate Target Iden2fica2on Clustering Discussant A9tude Profiles (DAPs)
- 38. 4-‐ Opinion-‐Target Pairing I totally disagree-‐ with you. The new immigra1on law is blatant-‐ racism-‐. Mary P2 D1 Topic1 nsubj(disagree-3, I-1) advmod(disagree-3, totally-2) root(ROOT-0, disagree-3) prep_with (disagree-3, you-5)Rule nsubj(racism-‐-4, Topic1-1) cop(racist-4, is-2) amod(racism-4, blatant-3) root(ROOT-0, racist-4) Rule
- 40. Candidate Targets 4-‐ Opinion-‐Target Pairing The new immigra1on law is good+. Illegal immigra1on is bad-‐. Peter I totally disagree-‐ with you. This law is blatant-‐ racism-‐. Mary Read all what Peter wrote. He is correct+. Illegal immigra1on is bad-‐ and must be stopped. John You are clueless-‐, Peter. Stop suppor2ng racism. Alexander P1 P2 P3 P4 D1 D2 D3 D4 D1 D1 D1 D1 Peter Topic1 Topic1 Topic2 Topic2 Topic 1 Topic 2
- 41. 4-‐ Opinion-‐Target Pairing • Language Uses (LUs) present in this step: – Targeted sen2ment toward other discussants (2nd person) – Targeted Sen2ment toward topic men2ons (3rd person) I totally disagree -‐ with you. This law is blatant -‐ racism -‐.
- 42. 4-‐ Opinion-‐Target Pairing • LU details – Rule-‐based detec2on of sen2ment targets (we’ve also been experimen2ng with supervised target detec2on methods) – Discussant targets are iden2fied by 2nd person pronouns (you, your, yourself, etc.) and by username men2ons (casper3912, etc.)
- 43. Subgroup Detec2on System Overview Discussion Thread Subgroups Discussant Opinion Expressions Iden2fica2on Thread Parsing … disagree…… …....... ………… like…………… ………………… bad……. Candidate Target Iden2fica2on ..........you……... ......................... ......conserva1ves ideologues………. ……………………… ....…..Immigra1on law………………… Opinion-‐Target Pairing disagree You like Conserva2ve Ideologues bad Immigra2on law Reply Structure Candidate Target Iden2fica2on Clustering Discussant A9tude Profiles (DAPs)
- 44. 5-‐ Discussant Aktude Profile Target1 ……… Targetn + -‐ # IA + -‐ # IA + -‐ # IA DAP1 DAP2 # IA is the number of interac2ons
- 45. 5-‐ Discussant Aktude Profile Peter Mary John Alexander Topic 1 Topic 2 Targets Discussants 0 0 0 0 0 0 1 0 1 0 0 0 1 0 1 0 1 1 0 0 0 0 0 0 0 1 1 1 0 1 0 2 2 0 0 0 0 0 0 1 0 1 1 0 2 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0
- 46. 5-‐ Discussant Aktude Profile Peter Mary John Alexander Topic 1 Topic 2 Targets Discussants 0 0 0 0 0 0 1 0 1 0 0 0 1 0 1 0 1 1 0 0 0 0 0 0 0 1 1 1 0 1 0 2 2 0 0 0 0 0 0 1 0 1 1 0 2 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 Each Discussant is implicitly posi1ve toward himself
- 47. Subgroup Detec2on System Overview Discussion Thread Subgroups Discussant Opinion Expressions Iden2fica2on Thread Parsing … disagree…… …....... ………… like…………… ………………… bad……. Candidate Target Iden2fica2on ..........you……... ......................... ......conserva1ves ideologues………. ……………………… ....…..Immigra1on law………………… Opinion-‐Target Pairing disagree You like Conserva2ve Ideologues bad Immigra2on law Reply Structure Candidate Target Iden2fica2on Clustering Discussant A9tude Profiles (DAPs)
- 48. Clustering Peter Mary John Alexander Subgroup 2 Subgroup 1 (Peter -‐, Topic1 -‐) (Peter -‐) (Topic1 +, Topic 2 -‐) (Peter +, Topic 2 -‐)
- 49. Evalua2on (Abu-‐Jbara et al., ACL 2012) (Abu-‐Jbara et al., ACL 2013)
- 50. English Data • 117 Discussions • Short threads • short posts • Human annota2on • More formal • 12 Polls + Discussions • Long threads • Long and short posts • Data self-‐labeled • Less formal • 30 debates • Long threads • Long and short posts • Data self-‐labeled • Less formal
- 52. Arabic Data • Forum for 2 sided self labeled poli2cal debates www.naqeshny.com • 36 debates comprising 711 posts corresponding to 326 users • • The average number of posts per discussion 19.75 and average number of discussants per topic 13.08
- 53. Evalua2on Metrics 1. Purity Source: hVp://nlp.stanford.edu/IR-‐book/html/htmledi2on/evalua2on-‐of-‐clustering-‐1.html
- 54. Evalua2on Metrics 2. Entropy 3. F-‐Measure where P(I, j) is the probability of finding an element from the category i in the cluster j, nj is the number of items in cluster j, and n the total number of items in the distribu2on.
- 55. Baselines • Interac2on Graph Clustering (GC) – Nodes: Par2cipants – Edges: interac2ons (connect two par2cipants if they exchange posts) • Text Classifica2on (TC) – Build TF-‐IDF vectors for each par2cipant (using all his/ her posts) – Cluster the vector space
- 56. English Clustering Algorithm • K-‐means • Expecta2on Maximiza2on (EM) • Farthest First (FF)
- 57. English Clustering Algorithm • K-‐means • Expecta2on Maximiza2on (EM) • Farthest First (FF)
- 58. Arabic Clustering Algorithm • K-‐means • Expecta2on Maximiza2on (EM) • Farthest First (FF)
- 59. Arabic Clustering Algorithm • K-‐means • Expecta2on Maximiza2on (EM): Purity 0.67 Entropy 0.72 (Best Results) • Farthest First (FF)
- 60. Comparison to baselines Our System English Results Arabic Results Method P E Signed Network 0.71 0.68 Our System 0.67 0.72
- 61. Wikipedia Poli1cal Forum Create debate Purity 0.66 0.61 0.64 Entropy 0.55 0.80 0.68 F-‐measure 0.61 0.56 0.60 English Results
- 62. Wikipedia Poli1cal Forum Create debate Purity 0.66 0.61 0.64 Entropy 0.55 0.80 0.68 F-‐measure 0.61 0.56 0.60 English Results Best performing
- 63. Wikipedia Poli1cal Forum Create debate Purity 0.66 0.61 0.64 Entropy 0.55 0.80 0.68 F-‐measure 0.61 0.56 0.60 English Results Best Performing & Worst Performing
- 64. Component Evalua2on Our System No Topical Targets No Discussant Targets No Sen1ment No Interac1on No Anaphora Resolu1on No Named En1ty Recog. No NP Chunking
- 65. Component Evalua2on Our System No Topical Targets No Discussant Targets No Sen1ment No Interac1on No Anaphora Resolu1on No Named En1ty Recog. No NP Chunking Not really a linguistic feature
- 66. Component Evalua2on Our System No Topical Targets No Discussant Targets No Sen1ment No Interac1on No Anaphora Resolu1on No Named En1ty Recog. No NP Chunking More of a linguistic feature!
- 67. Deeper look at Agreement/ Disagreement • So far we employed shared/divergent opinion in the form of explicit polarity indicators – Sen2ment polarity towards other discussants • A: So, no maHer how much faith you have, one of you MUST be wrong! (nega.ve) • B: You are a scien.st?! May I ask in which field? (nega.ve) – Sen2ment polarity towards an en.ty • A: Here is an excellent verse from the Bible.. (posi.ve) • B: The Bible rightly says that... (posi.ve)
- 68. Implicit Opinion/Perspec2ve • Observa2on: People sharing similar beliefs/perspec2ve tend to use the same evidence to support their point – Believers: faith, peace, love, ci2ng verses from the Bible... – Atheists: reason, science, aVack on the “logical” flaws in Bible... • However it is not always explicit (using similar words and similar aktudes) • Peter: God is the creator of mankind • Mary: The belief in an ul2mate divine being has sustained me over the years – Not necessarily posi2ve/nega2ve – High dimensional similarity between both sentences is low! – BUT we know Mary and Peter share the same perspec1ve and will tend to be in agreement with each other
- 69. Modeling of implicit agreement/ disagreement • Implicit agreement or disagreement (perspec2ve) – using text similarity to help iden2fy subgroups • Perspec2ve modeling is used to complement explicit aktude • Perspec2ve granularity has to be collected on the level of a thread rather than a single post • Hence we summarize all the posts in the thread.
- 70. Our Model • Explicit high dimensional aktude toward other discussants and en22es • Modeling shared perspec2ve among discussants over threads using textual similarity on the post level in the latent space
- 71. Extrac2ng implicit perspec2ve • Run Latent Dirichlet Alloca2on(LDA) on the thread • Extract the topic distribu2on of each post • Aggregate the distribu2ons of all posts between each pair of discussants
- 72. FEATURE REPRESENTATION: ATTITUDE PROFILES • Vector Representa2on • Explicit aktude towards other discussants and En22es A B C E1 E2 A 0 0 0 1 1 2 0 1 1 1 0 1 0 0 0 B … C -‐-‐
- 73. FEATURE REPRESENTATION: ATTITUDE PROFILES • Vector Representa2on • Implicit agreement with other discussants A B C E1 E2 A B C A 0 0 0 1 1 2 0 1 1 1 0 1 0 0 0 1 1 1 1 0 0.5 0.5 0 0 B … C -‐-‐ 1 1 1
- 74. Data • English – Create Debate (CD) • www.createdebate.com • Deba2ng on a certain topic • Sides are explicitly indicated by discussants in a poll Informal language – Wikipedia Discussion Forum (WIKI) • en.wikipedia.org • Groups labels are manually annotated • Formal language, not much nega2ve polarity • Arabic – www.naqeshny.com – Self labeled poli2cal debates
- 75. Experimental Condi2ons • Clustering algorithm – S-‐Link # of clusters by rule of thumb = √n/2 • Evalua2on Metrics – Purity, Entropy, F-‐measure • Baseline – RAND-‐BASE: Assign discussants to clusters randomly – SWD-‐BASE: Calculate surface word distribu2on, as a simpler form of perspec2ve
- 76. English Results Condi1on Wiki CD Purity Entropy Fmeasure Purity Entropy Fmeasure RAND-‐BASE 0.675 0.563 0.652 0.399 0.966 0.41 SWD-‐BASE 0.772 0.475 0.646 0.452 0.932 0.432 SD 0.834 0.360 0.667 0.824 0.394 0.596 SE 0.827 0.383 0.655 0.793 0.422 0.582 SD+SE 0.835 0.362 0.665 0.82 0.385 0.604 PERS 0.853 0.321 0.699 0.787 0.399 0.589 SD+PERS 0.853 0.320 0.698 0.849 0.333 0.615 SE+PERS 0.853 0.321 0.702 0.789 0.399 0.591 SD+SE+PERS 0.857 0.310 0.703 0.861 0.315 0.625
- 77. Observa2ons Condi1on Wiki CD Purity Entropy Fmeasure Purity Entropy Fmeasure RAND-‐BASE 0.675 0.563 0.652 0.399 0.966 0.41 SWD-‐BASE 0.772 0.475 0.646 0.452 0.932 0.432 SD 0.834 0.360 0.667 0.824 0.394 0.596 SE 0.827 0.383 0.655 0.793 0.422 0.582 SD+SE 0.835 0.362 0.665 0.82 0.385 0.604 PERS 0.853 0.321 0.699 0.787 0.399 0.589 SD+PERS 0.853 0.320 0.698 0.849 0.333 0.615 SE+PERS 0.853 0.321 0.702 0.789 0.399 0.591 SD+SE+PERS 0.857 0.310 0.703 0.861 0.315 0.625 Best Performance is when we combine explicit aktude (SD Sen2ment toward other discussants, SE Sen2ment toward En22es) with implicit perspec2ve (PERS), regardless of genre
- 78. Observa2ons Condi1on Wiki CD Purity Entropy Fmeasure Purity Entropy Fmeasure RAND-‐BASE 0.675 0.563 0.652 0.399 0.966 0.41 SWD-‐BASE 0.772 0.475 0.646 0.452 0.932 0.432 SD 0.834 0.360 0.667 0.824 0.394 0.596 SE 0.827 0.383 0.655 0.793 0.422 0.582 SD+SE 0.835 0.362 0.665 0.82 0.385 0.604 PERS 0.853 0.321 0.699 0.787 0.399 0.589 SD+PERS 0.853 0.320 0.698 0.849 0.333 0.615 SE+PERS 0.853 0.321 0.702 0.789 0.399 0.591 SD+SE+PERS 0.857 0.310 0.703 0.861 0.315 0.625 Wiki seems to gain more from implicit perspec2ve compared to CD Explicit Aktude is a beVer feature for CD: people express their sen2ments openly, while in Wiki people are more constrained and subtle in their expressions
- 79. Observa2ons Condi1on Wiki CD Purity Entropy Fmeasure Purity Entropy Fmeasure RAND-‐BASE 0.675 0.563 0.652 0.399 0.966 0.41 SWD-‐BASE 0.772 0.475 0.646 0.452 0.932 0.432 SD 0.834 0.360 0.667 0.824 0.394 0.596 SE 0.827 0.383 0.655 0.793 0.422 0.582 SD+SE 0.835 0.362 0.665 0.82 0.385 0.604 PERS 0.853 0.321 0.699 0.787 0.399 0.589 SD+PERS 0.853 0.320 0.698 0.849 0.333 0.615 SE+PERS 0.853 0.321 0.702 0.789 0.399 0.591 SD+SE+PERS 0.857 0.310 0.703 0.861 0.315 0.625 BeVer results obtained on the same data set from the previous results for Wiki (P 0.66, E 0.55) CD (P 0.64, E 0.68)
- 80. Arabic Results Using EM Purity Entropy F-‐measure Signed Network BASELINE 0.71 0.68 0.67 Explicit Aktude 0.67 0.72 0.65 Implicit/Perspec2ve 0.64 0.74 0.65 Our System (combined) 0.77 0.50 0.76
- 81. Arabic Results Using EM Purity Entropy F-‐measure Signed Network BASELINE 0.71 0.68 0.67 Explicit Aktude 0.67 0.72 0.65 Implicit/Perspec2ve 0.64 0.74 0.65 Our System (combined) 0.77 0.50 0.76 Significant improvement over baseline
- 82. Arabic Results Using EM Purity Entropy F-‐measure Signed Network BASELINE 0.71 0.68 0.67 Explicit Aktude 0.67 0.72 0.65 Implicit/Perspec2ve 0.64 0.74 0.65 Our System (combined) 0.77 0.50 0.76 Significant improvement over baseline Complementarity between Explicit aktude and Perspec2ve
- 83. Conclusions • We can successfully model sociopragma2c phenomena – Golden rule of computer science (divide and conquer) Form subgroups J • There is significant room for improvement • It takes a large team of computer scien2sts and significant collabora2on with the humani2es to get this program going
- 84. Where are we now? • Extensive work on Sen2ment and Emo2on Intensity characteriza2on/detec2on • Work on Rumor Detec2on • Work on Level of CommiVed Belief Tagging (check us out at *SEM 2015, and EXPROM 2015) • Work on Ideological Perspec2ve Detec2on (check us out at *SEM 2015)