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JenLing Workshop Jena, Germany, February 8, 2019 Using Artificial Neural Networks to Model Affective Word Meaning Sven Buechel Jena University Language and Information Engineering (JULIE) Lab Friedrich-Schiller-Universitt Jena, Jena,

  1. JenLing Workshop Jena, Germany, February 8, 2019 Using Artificial Neural Networks to Model Affective Word Meaning Sven Buechel Jena University Language and Information Engineering (JULIE) Lab Friedrich-Schiller-Universität Jena, Jena, Germany https://julielab.de Using Artificial Neural Networks to Model Affective Word Meaning 1 Sven Buechel

  3. sunshine

  5. calm

  7. terrorism

  9. JenLing Workshop Jena, Germany, February 8, 2019 What is „Affective Word Meaning“? • Psycholinguistic quality to evoke emotion in recipients • Speakers mostly agree on it Ø part of connotative lexical semantics • Graphematic word (type), mere character sequences • No context! Using Artificial Neural Networks to Model Affective Word Meaning 9 Sven Buechel

  10. JenLing Workshop Jena, Germany, February 8, 2019 Application Domains • Product and enterprise analytics • Social sciences • voting behavior / approval rate • happiness across geographic/socio-economic positions • Humanities • Amelioration/pejoration of words • Attitudes towards concepts and ideas • Emotional relationships in character network rottentomatoes.com twitter.com Using Artificial Neural Networks to Model Affective Word Meaning 10 Sven Buechel

  11. JenLing Workshop Jena, Germany, February 8, 2019 Application Domains • Product and enterprise analytics • Social sciences • voting behavior • happiness across geographic/socio-economic position • Humanities • amelioration/pejoration of words • attitudes towards concepts and ideas • emotional relationships in character networks Using Artificial Neural Networks to Model Affective Word Meaning 11 Sven Buechel

  12. JenLing Workshop Jena, Germany, February 8, 2019 Goal of This Work Word Input Neural Network Using Artificial Neural Networks to Model Affective Word Meaning 12 Sven Buechel

  13. JenLing Workshop Jena, Germany, February 8, 2019 Goal of This Work ??? Word Input Neural Network Using Artificial Neural Networks to Model Affective Word Meaning 13 Sven Buechel

  14. JenLing Workshop Jena, Germany, February 8, 2019 How to Represent Affective Word Meaning? Using Artificial Neural Networks to Model Affective Word Meaning 14 Sven Buechel

  15. JenLing Workshop Jena, Germany, February 8, 2019 Semantic Orientation / Polarity + Word – Using Artificial Neural Networks to Model Affective Word Meaning 15 Sven Buechel

  16. JenLing Workshop Jena, Germany, February 8, 2019 Ekman’s Basic Emotions Source: http://ocw.mit.edu/courses/brain-and-cognitive-sciences/9-00sc-introduction-to-psychology-fall-2011/emotion-motivation/discussion-emotion/ Using Artificial Neural Networks to Model Affective Word Meaning 16 Sven Buechel

  17. JenLing Workshop Jena, Germany, February 8, 2019 Representing Emotion — Wheel of Emotion Source: https://en.wikipedia.org/wiki/Contrasting_and_categorization_of_emotions#/media/File:Plutchik-wheel.svg Using Artificial Neural Networks to Model Affective Word Meaning 17 Sven Buechel

  18. JenLing Workshop Jena, Germany, February 8, 2019 Valence-Arousal-Dominance (Russell & Mehrabian, 1977) 1.0 Joy ● Anger ● (being controlled—in control) Disgust ● Surprise ● 0.5 Dominance Fear ● 0.0 Sadness ● 1.0 0.5 ) t − 0.5 n Arousal e 0.0 m e t i − 0.5 c x e — − 1.0 − 1.0 s s e − 1.0 − 0.5 0.0 0.5 1.0 n m l Valence a c ( (displeasure—pleasure) Using Artificial Neural Networks to Model Affective Word Meaning 18 Sven Buechel

  19. JenLing Workshop Jena, Germany, February 8, 2019 Empirically Measured VAD Ratings • Psychologists and Psycholinguists need VAD ratings (e.g., experiments on word processing and memory) • Experimental set-up of gathering those • questionnaire study • >20 raters per word Using Artificial Neural Networks to Model Affective Word Meaning 19 Sven Buechel

  20. JenLing Workshop Jena, Germany, February 8, 2019 Self-Assessment Manikin 1 2 3 4 5 6 7 8 9 V A D Using Artificial Neural Networks to Model Affective Word Meaning 20 Sven Buechel

  21. JenLing Workshop Jena, Germany, February 8, 2019 Averaged Individual Ratings: Emotion Lexicons Valence Arousal Dominance sunshine 7.6 4.9 5.2 calm 6.3 1.9 5.9 terrorism 1.5 8.4 3.2 Using Artificial Neural Networks to Model Affective Word Meaning 21 Sven Buechel

  22. JenLing Workshop Jena, Germany, February 8, 2019 How to Model Affective Word Meaning? Using Artificial Neural Networks to Model Affective Word Meaning 22 Sven Buechel

  23. JenLing Workshop Jena, Germany, February 8, 2019 Input Representation: Word Embeddings dog cat turtle Computational Input Model pie Using Artificial Neural Networks to Model Affective Word Meaning 23 Sven Buechel

  24. JenLing Workshop Jena, Germany, February 8, 2019 -0.13102 -0.054447 -0.051866 -0.10289 -0.072061 0.16523 -0.17298 0.21865 0.041183 -0.010858 0.074741 0.35226 0.42662 -0.071747 0.25112 0.12082 -0.33192 -0.4728 -0.0090568 0.0030266 0.032861 0.074323 -0.38017 0.091399 -0.16034 -0.050232 -0.094194 0.16656 0.40901 0.069625 0.059306 0.01991 -0.35846 -0.14549 0.24894 0.50184 - 0.0073098 -0.4589 -0.10073 -0.099315 0.30583 -0.40577 0.16586 0.055741 0.26776 -0.13515 0.28127 0.069221 - 0.20907 0.092053 0.39419 -0.2412 0.01173 -0.16856 -0.0053851 0.14282 0.17513 0.34775 0.178 0.35883 -0.17684 0.53104 0.04751 -0.30134 -0.53297 -0.22041 0.097703 0.052288 0.10849 0.12409 -0.11369 0.19042 0.19554 - 0.14949 -0.29675 -0.14285 0.22217 0.21503 -0.2309 0.4381 0.22739 -0.052386 -0.20003 0.19725 -0.032432 - 0.14307 0.021958 0.36876 -0.10084 -0.18536 0.27691 -0.43856 0.087418 -0.33836 0.083161 -0.40672 0.14497 - 0.41334 0.0012195 -0.32266 0.067225 0.18359 0.010442 -0.15499 -0.82943 -0.069867 -0.26416 0.42656 0.26765 - 0.12262 -0.116 -0.076926 -0.16992 0.055428 -0.20699 -0.090381 0.082171 -0.31509 -0.12135 0.055464 0.9075 0.18585 -0.20836 0.019945 0.17853 -0.31707 0.054172 0.40715 0.32685 -0.20493 0.099457 0.15329 -0.28035 0.36088 0.31671 -0.2216 -0.094332 0.33993 -0.23604 0.44507 -0.025739 0.2082 -0.28423 0.18867 -0.30867 - 0.015983 0.13985 0.035387 0.25648 -0.18241 0.50119 -0.31602 -0.19771 -0.3002 0.048059 0.14868 -0.45165 0.11831 0.045376 0.31328 -0.052771 0.08615 -0.18376 0.071614 0.30406 0.26742 -0.22895 0.17671 0.33062 0.17738 0.042157 -0.29211 -0.10786 -0.064557 -0.10006 0.39087 -0.21173 -0.085387 -0.040239 -0.1044 -0.019623 - 0.32887 0.15656 0.039189 -0.30531 0.235 -0.025831 0.041146 0.30737 -0.16955 -0.18446 -0.11642 0.038028 0.094888 -0.25135 -0.011466 0.18069 0.44957 -0.28939 -0.46813 0.035372 0.045633 0.1507 -0.098108 -0.31644 - 0.19265 -0.3108 0.32345 0.57775 0.042428 0.2334 -0.093899 -0.50785 -0.68498 0.088108 -0.25361 -0.018187 - 0.50159 -0.19892 -0.12127 -0.21447 0.22551 0.021314 0.078556 -0.0828 -0.27046 -0.19486 0.13457 0.44123 0.13542 -0.37831 0.36109 -0.04392 0.21795 -0.092712 -0.12707 -0.1428 -0.021229 -0.13407 -0.12783 -0.099737 - 0.055585 0.042925 -0.41051 -0.044614 -0.2326 -0.033486 -0.1761 -0.042099 -0.20191 -0.042496 -0.08971 0.062699 -0.39227 0.2632 0.13261 -0.45002 -0.2213 0.31223 0.43488 -0.05547 0.22954 0.70868 -0.37327 0.2844 -0.24495 - 0.28255 0.21883 -0.053093 -0.3006 -0.34203 -0.11602 0.36381 0.11346 0.1853 -0.014843 0.21921 0.047219 - 0.0054492 0.2878 0.51144 0.17271 -0.026182 0.00051472 0.033597 -0.061401 0.25367 -0.13141 -0.056602 - 0.0025169 0.44398 -0.26233 0.21532 0.34318 -0.081855 -0.030759 -0.022955 -0.1757 0.44088 -0.062219 Using Artificial Neural Networks to Model Affective Word Meaning 24 Sven Buechel

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