/FontDescriptor 375 0 R /FontBBox [ -39 -250 1036 750 ] 95 0 obj /A << 200 0 obj 1027.8 799.4 799.4 1027.8 1027.8 513.9 513.9 ] /D [ 303 0 R /XYZ 52.158 772.725 null ] /D [ 323 0 R /XYZ 344.957 203.61 null ] /A << /D (subsubsection.2.2.3) /H /I << /Type /Annot << endobj /ProcSet [ /PDF /Text /ImageC ] << 285 0 obj /S /GoTo /Subtype /Link /Flags 4 /D (Hfootnote.16) Thus far, nearly all research has been based on the ANEW norms collected by Bradley and Lang (1999) for 1,034 words. /Flags 4 << >> /Type /Annot /Type /Annot >> /C [ 1 0 0 ] << /Type /Annot /Rect [ 228.017 371.597 233.994 384.118 ] /A << >> 388 0 obj >> endobj /D [ 303 0 R /XYZ 47.176 607.058 null ] /D (subsubsection.2.2.3) /Border [ 0 0 1 ] sions valence, arousal and dominance. /F11 157 0 R /Resources 322 0 R /Annots [ 310 0 R 311 0 R 320 0 R 321 0 R ] << 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 endobj << 412 0 obj stream << /D (table.3) endobj endobj 123 0 obj endobj endobj /D 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<< /Next 76 0 R >> >> /XHeight 431 35 0 obj /S /GoTo << /D [ 303 0 R /XYZ 47.176 557.751 null ] >> 217 0 obj /FontDescriptor 377 0 R 379 0 obj /D (cite.flynn2014) 108 0 obj /D (cite.SemEval2018Task1) endobj /A << << Mining Valence, Arousal, and Dominance - Possibilities for Detecting Burnout and Productivity? /Border [ 0 0 1 ] endobj >> /A << /Descent -194 endobj Experiments and Results 4.1. /H /I 40 0 obj /Title 53 0 R << endobj endobj >> /Rect [ 86.683 311.822 249.887 322.765 ] /CapHeight 669 << >> /First 16 0 R endobj /D [ 323 0 R /XYZ 304.866 160.064 null ] 139 0 R ] endobj 36 0 obj endobj /FontName /YOFOEL+CMR9 For a given word and a dimension (V/A/D), the scores range from 0 (lowest V/A/D) to 1 (highest V/A/D). /H /I >> /LastChar 118 166 0 R (cite.mikolov2013efficient) 182 0 R (cite.russell2003core) 135 0 R (cite.yu2016building) /Rect [ 98.234 407.474 149.108 418.418 ] 309 0 obj >> << 405 0 obj 386 0 obj >> 199 0 obj /ItalicAngle 0 endobj /H /I /Type /Annot >> << << endobj >> /Type /Page /D [ 303 0 R /XYZ 47.176 447.182 null ] (EI-reg and V-reg Annotations) /Subtype /Type1 /D (cite.maxdiff-naacl2016) /D [ 332 0 R /XYZ 92.559 138.293 null ] >> /CharSet (/E/N/greater/i/j/kappa/n/period/r) 1000 1000 777.8 275 1000 666.7 666.7 888.9 888.9 0 0 555.6 555.6 666.7 500 722.2 /A << /Subtype /Link /Parent 274 0 R endobj /D (table.10) /C [ 1 0 0 ] /D (table.3) >> This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. >> AffectNet is one of the largest datasets for facial affect in still images which covers both … /F61 124 0 R /D (cite.maxdiff-naacl2016) /Title 65 0 R >> endobj /Subtype /Link 276 0 obj /C [ 1 0 0 ] /A 66 0 R << >> /FirstChar 20 /S /GoTo 118 0 obj 139 0 obj /Subtype /Link /S /GoTo /LastChar 118 /Subtype /Link endobj endobj 50 0 obj endobj 263 0 obj /D (cite.kuder1937theory) /C [ 0 1 0 ] endobj << << endobj << endobj /Subtype /Link /D (subsection.6.5) endobj 207 0 obj endobj >> 288 0 obj endobj << endobj /Type /Page 312 0 obj /D (Hfootnote.12) endobj 174 0 obj /D (cite.MohammadB17wassa) /H /I /Type /FontDescriptor 16 0 obj /S /GoTo /F14 121 0 R /D (figure.3) 353 0 obj 267 0 obj dimensions (valence, arousal, dominance) and discrete emotions (anger, fear, sadness, joy). 290 0 obj /A 82 0 R 87 0 obj /Type /FontDescriptor /Subtype /Link /Border [ 0 0 1 ] /Ascent 669 /Title 61 0 R >> >> endobj /C [ 1 0 0 ] /Type /FontDescriptor /Parent 8 0 R /Encoding 348 0 R /Rect [ 80.389 121.303 87.363 132.246 ] >> [ 777.8 277.8 777.8 500 777.8 500 777.8 777.8 777.8 777.8 777.8 777.8 777.8 1000 /H /I /Type /Page >> /D [ 313 0 R /XYZ 91.623 253.683 null ] /C [ 0 1 0 ] << /Subtype /Link << >> /H /I 137 0 obj 149 0 obj >> 76 0 obj /XObject << << >> /S /GoTo >> 30 0 obj >> endobj >> 193 0 obj This dataset contains 1182 images that have been labeled along the three dimensions of valence, arousal, and dominance to indicate emotional reactions. 187 0 obj /Subtype /Link /Subtype /Type1 << 120 0 obj /Type /Font endobj endobj /Type /Annot /S /GoTo >> /D (subsection.6.3) /S /GoTo << /CapHeight 683 /D (cite.MohammadB17starsem) << The NRC Valence, Arousal, and Dominance (VAD) Lexicon includes a list of more than 20,000 English words and their valence, arousal, and dominance scores. 375 0 obj /FontName /SALDCW+CMR6 500 277.8 277.8 277.8 777.8 ] endobj /Border [ 0 0 1 ] 511.1 460 421.7 408.9 332.2 536.7 460 ] << >> /S /GoTo /C [ 0 1 0 ] /D [ 234 0 R /XYZ 68.297 107.519 null ] /FontName /QRTGJK+CMSY9 /Limits [ (page.10) (page.4) ] << >> This dataset has been used for multimodal analysis and recognition of human emotions. /Border [ 0 0 1 ] /CapHeight 683 /A << /Contents [ 439 0 R 292 0 R 438 0 R 440 0 R ] >> 295 0 obj /StemV 74 332 0 obj << /Rect [ 200.32 587.543 288.004 597.734 ] 343 0 obj /Type /Annot /Type /Annot 202 0 obj /Type /FontDescriptor << /A << /C [ 1 0 0 ] /H /I /S /GoTo /F8 158 0 R /Type /Annot /A 58 0 R 47 0 obj /D (cite.MohammadB17wassa) << /Type /Annot /StemV 69 /Descent -194 >> 371 0 obj >> /H /I /Subtype /Link /C [ 1 0 0 ] /Type /Annot >> /H /I << endobj 42 0 obj /Type /Annot >> /Limits [ (Hfootnote.4) (Hfootnote.9) ] >> /Parent 68 0 R 82 0 obj 15 0 obj << /Widths 345 0 R /Descent -194 These scores are well above the SHR scores obtained byWarriner et al. >> >> >> author={Mohammad, Saif M.}, /Widths 352 0 R /Widths 353 0 R /Rect [ 355.895 150.835 372.831 161.779 ] /Type /Annot endobj >> /S /GoTo The EMOTIC dataset, named after EMOTions In Context, is a database of images with people in real environments, annotated with their apparent emotions. /Rect [ 167.611 363.411 217.957 374.354 ] /Border [ 0 0 1 ] 88 0 obj /FontDescriptor 373 0 R endobj /QQAPF6907e3b5 418 0 R /ProcSet [ /PDF /Text ] lexicon_afinn(), >> /C [ 1 0 0 ] /Type /FontDescriptor (E-c Annotations) 69 0 obj /Resources 116 0 R 171 0 obj 407 0 obj /D [ 151 0 R /XYZ 51.158 773.725 null ] /Border [ 0 0 1 ] /CharSet (/check) /D [ 263 0 R /XYZ 52.158 628.242 null ] /F43 217 0 R >> 275 0 obj /Rect [ 349.103 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51.158 773.725 null ] 13 0 obj /FontFile 366 0 R /Parent 48 0 R /Flags 4 /D (cite.maxdiff-naacl2016) /Descent -209 /Subtype /Link [ 892.9 ] /D (subsection.3.1) /Flags 4 endobj 128 0 obj /A << 181 0 R (table.6) 267 0 R ] /Rect [ 90.722 383.564 100.187 396.085 ] << 89 0 obj /C [ 1 0 0 ] /Rect [ 400.543 576.691 467.593 586.882 ] /S /GoTo /S /GoTo /Resources 312 0 R /F8 158 0 R endobj /C [ 0 1 0 ] << << /F14 121 0 R endobj >> << /A << Three components of emotions are traditionally distinguished: valence (the pleasantness of a stimulus), arousal (the intensity of emotion provoked by a stimulus), and dominance (the degree of control exerted by a stimulus). 204 0 obj 209 0 obj << /D [ 187 0 R /XYZ 52.158 608.365 null ] /Title 57 0 R 177 0 obj /D (subsubsection.2.1.3) /Subtype /Link 351 0 obj /Subtype /Link 8 0 obj 51 0 R (subsection.3.2) 55 0 R (subsection.6.1) 71 0 R ] /Next 28 0 R /C [ 0 1 0 ] 351.8 384.3 643.5 351.8 1000 675.9 611.1 675.9 643.5 481.5 488 481.5 675.9 643.5 /Type /Annot /A << endobj /H /I 286 0 obj endobj /Count -3 endobj /D (table.11) >> >> (Compiling Tweets) /S /GoTo >> /H /I 131 0 obj /Rect [ 229.762 375.366 285.235 386.31 ] /D [ 303 0 R /XYZ 299.885 520.207 null ] /S /GoTo endobj /Subtype /Link 352 0 obj /A << /H /I /Rect [ 514.179 84.702 523.643 97.124 ] /F54 119 0 R /A << /D (Hfootnote.11) >> /Subtype /Link /Title 37 0 R endobj /ProcSet [ /PDF /Text /ImageC ] endobj /FontName /PXBGHL+CMSY7 /Type /FontDescriptor /Type /Annot endobj /Type /Annot /C [ 1 0 0 ] [ 388.9 388.9 500 777.8 277.8 333.3 277.8 500 500 500 500 500 500 500 500 500 500 220 0 obj endobj /S /GoTo /D (cite.yu2016building) /Rect [ 271.068 313.478 288.004 324.422 ] << /A << >> Table 4 shows the V–A, A–D, and D–V correlations for the words in PoKi, poems by adults, and also for all the words in the NRC VAD Lexicon. 411 0 obj /D (cite.AlmRS05) /Font << >> /QQAPGScb572671 419 0 R /S /GoTo 392 0 obj << << /Parent 274 0 R << /H /I /Rect [ 220.616 193.034 288.004 203.977 ] /Border [ 0 0 1 ] 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Emotions are difficult to pin down, which for some creates a desire to use non-specific or non-restricted to language when describing these states. /BaseFont /RXNEKG+MSAM10 367 0 obj /Type /Annot /Subtype /Link /PageMode /UseOutlines >> /H /I /Descent -194 /Limits [ (cite.frijda1988laws) (subsection.6.1) ] lexicon_nrc(). /S /GoTo dataset DEAP, is detailed in [21]. /H /I 34 0 obj << /S /GoTo << 631 507.9 631 507.9 354.2 569.5 631 323.4 354.2 600.2 323.4 938.5 631 569.5 631 600.2 >> /Names [ (Hfootnote.14) 269 0 R (Hfootnote.15) 273 0 R (Hfootnote.16) 298 0 R (Hfootnote.17) endobj >> 341 0 obj /Names [ (cite.Louviere_1991) 164 0 R (cite.MohammadB17starsem) 143 0 R (cite.MohammadB17wassa) << /C [ 0 1 0 ] >> /D (section.3) 722 722 333 389 722 611 889 722 722 556 722 667 556 611 722 722 944 722 722 611 333 << In Proceedings of the 56th Annual /FontName /RXNEKG+MSAM10 << /D (cite.maxdiff-naacl2016) /Rect [ 166.213 604.206 173.186 613.132 ] [ 511.1 460 460 511.1 460 306.7 460 511.1 306.7 306.7 460 255.6 817.8 562.2 511.1 /H /I /Annots [ 207 0 R 222 0 R 223 0 R 224 0 R 225 0 R 226 0 R 230 0 R 231 0 R 232 0 R ] /Type /Font /F54 119 0 R >> /Type /Annot /C [ 0 1 0 ] << /Rect [ 186.303 500.139 192.281 512.563 ] /FontFile 378 0 R Source: Multi-attention Recurrent Network for Human Communication Comprehension. << /Limits [ (section.1) (section.6) ] endobj endobj /D (subsection.6.4) /Border [ 0 0 1 ] >> 406 0 obj /CapHeight 683 169 0 obj endobj /Type /Encoding /Type /Annot >> [ 446.4 446.4 569.5 877 323.4 384.9 323.4 569.5 569.5 569.5 569.5 569.5 569.5 569.5 >> /D (cite.LREC18-AIL) 404 0 obj First, the two dimensions of the Circumplex model (i.e., valence and arousal… >> 500 500 500 500 500 500 500 500 500 333 333 570 570 570 500 930 722 667 722 722 667 /S /GoTo /Limits [ (subsection.6.2) (subsubsection.2.1.2) ] << /Subtype /Link /S /GoTo >> << >> /Kids [ 403 0 R 404 0 R 405 0 R 406 0 R ] /Type /Annot /Parent 48 0 R (The Affect in Tweets Dataset) /XHeight 426 /H /I endobj /Subtype /Link (Annotating Tweets) >> /Title 41 0 R /D (subsubsection.2.1.1) endobj /H /I /Rect [ 346.49 245.944 370.898 254.871 ] /Type /Annot /H /I endobj >> /Title 17 0 R << The EMOTIC dataset, named after EMOTions In Context, is a database of images with people in real environments, annotated with their apparent emotions.The images are annotated with an extended list of 26 emotion categories combined with the three common continuous dimensions Valence, Arousal and Dominance. /S /GoTo >> /Border [ 0 0 1 ] >> endobj >> /Type /Annot >> >> We also >> 90 0 obj /Subtype /Link /D (Hfootnote.4) 208 0 obj The authors created this dataset as part of … 347 0 obj /Last 68 0 R /A << /Flags 4 endobj /H /I /D (cite.Parrot01) << /S /GoTo /Count 12 /Rect [ 332.861 132.424 339.835 143.367 ] /Contents [ 424 0 R 178 0 R 423 0 R 425 0 R ] /C [ 0 1 0 ] >> /C [ 0 1 0 ] 121 0 obj /Type /Annot /A << /S /GoTo /CapHeight 651 /Border [ 0 0 1 ] /Subtype /Link /FontName /IRGUDN+CMSY10 >> >> /H /I /FirstChar 40 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endobj >> endobj << /CapHeight 683 The tags were collected with both paper-and-pencil and computer-based versions of SAM, using a 9-point rating scale for each dimension. /A << 345 0 obj /Subtype /Link /FirstChar 2 << /S /GoTo 29 0 obj 45 0 obj 350 0 obj /ItalicAngle -14 /A << /Border [ 0 0 1 ] >> /Subtype /Link /Pages 389 0 R endobj << /ItalicAngle 0 /Border [ 0 0 1 ] << endobj endobj For rese… /Parent 390 0 R /BaseFont /MPEZSC+CMR10 /D (subsection.6.1) 73 0 obj /S /GoTo /StemV 85 /C [ 0 1 0 ] /F54 119 0 R /S /GoTo /A << /S /GoTo /ItalicAngle -14 endobj >> endobj >> endobj /FontFile 387 0 R /FontFile 385 0 R @article{Mntyl2016MiningVA, title={Mining Valence, Arousal, and Dominance - Possibilities for Detecting Burnout and Productivity? >> 71 0 obj endobj /C [ 1 0 0 ] /Subtype /Link << endobj >> /D [ 177 0 R /XYZ 52.158 772.725 null ] endobj /Parent 130 0 R /Type /Annot /A << /Subtype /Link Logical, set TRUE if you have manually >> 395 0 obj endobj /Rect [ 95.733 44.845 163.475 56.24 ] endobj 591.4 828.1 517 362.8 654.2 1000 1000 1000 1000 277.8 277.8 500 500 500 500 500 500 endobj /D [ 151 0 R /XYZ 96.278 678.06 null ] /S /GoTo endobj /H /I /Subtype /Link /ItalicAngle -14 >> /Group 228 0 R /Next 80 0 R /D [ 234 0 R /XYZ 344.746 272.17 null ] /QQAPGScb572671 419 0 R /Names [ (cite.AlmRS05) 138 0 R (cite.Ekman92) 131 0 R (cite.Fleiss1971) 275 0 R (cite.KiritchenkoM2017bwsvsrs) << endobj >> >> endobj /Border [ 0 0 1 ] 308 0 obj /LastChar 0 44 0 obj << /A << << >> /C [ 1 0 0 ] /C [ 1 0 0 ] endobj /F57 122 0 R >> << We use non-parametric regressions to model developmental differences from early childhood to late-adolescence. /S /GoTo << /C [ 0 1 0 ] endobj /S /GoTo /C [ 1 0 0 ] endobj /FontFile 362 0 R >> /Subtype /Type1 /Border [ 0 0 1 ] /Type /Annot 349 0 obj 251 0 obj << /C [ 0 1 0 ] 104 0 obj (Bibliographical References) /FontDescriptor 371 0 R /Widths 347 0 R /C [ 1 0 0 ] 249 0 obj << /Type /Font endobj /Border [ 0 0 1 ] /Parent 390 0 R /H /I endobj << 107 0 obj 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/Type /Annot << /Subtype /Link >> << /D (cite.Ekman92) << all ratings were below the median of valence/dominance and above the median of arousal in the entire dataset (shown as dotted line). /D [ 209 0 R /XYZ 51.158 773.725 null ] /Subtype /Link /XHeight 431 /F56 120 0 R /Rect [ 435.5 222.566 442.474 233.579 ] /Parent 274 0 R /Descent -194 /Subtype /Link /Subtype /Link 224 0 obj >> /Type /Pages /S /GoTo /Type /Annot /Border [ 0 0 1 ] /D [ 187 0 R /XYZ 68.297 63.684 null ] endobj /Limits [ (cite.Parrot01) (cite.flynn2014) ] /S /GoTo endobj /D [ 313 0 R /XYZ 304.866 191.895 null ] >> endobj >> 278 389 422 500 333 500 500 444 500 444 278 500 500 278 278 444 278 722 500 500 500 /D (Hfootnote.15) 241 0 obj /A << >> >> >> /Contents [ 430 0 R 210 0 R 429 0 R 431 0 R ] /Subtype /Link /Parent 28 0 R /D [ 303 0 R /XYZ 47.176 88.59 null ] << /D [ 263 0 R /XYZ 51.158 773.725 null ] /Type /Annot endobj Both the categorical model and the dimensional model of emotions have a large body of work supporting them, endobj << /Border [ 0 0 1 ] /D [ 209 0 R /XYZ 88.003 721.826 null ] >> /S /GoTo 320 0 obj /D [ 263 0 R /XYZ 68.297 96.775 null ] 122 0 obj /FontBBox [ -29 -960 1116 775 ] /Rect [ 521.565 504.192 540.713 515.136 ] << >> /XHeight 450 << /Parent 130 0 R << /BaseFont /YOFOEL+CMR9 /F14 121 0 R /Rect [ 262.861 397.165 269.835 408.108 ] endobj /Rect [ 345.078 467.721 352.052 478.665 ] lexicon_nrc_eil(), /H /I /ItalicAngle 0 /Last 56 0 R /A << endobj /Border [ 0 0 1 ] /Subtype /Type1 /CharSet (/arrowdown/arrowup/multiply) >> /D [ 303 0 R /XYZ 47.176 348.568 null ] /D [ 151 0 R /XYZ 321.006 107.734 null ] /Names [ (subsubsection.2.1.3) 23 0 R (subsubsection.2.2.1) 31 0 R (subsubsection.2.2.2) >> /D (section.4) /A << /D (cite.flynn2014) [ 556 556 167 333 667 278 333 333 0 333 570 0 667 444 333 278 0 0 0 0 0 0 0 0 0 0 /C [ 1 0 0 ] /Subtype /Link /Type /Annot endobj 61 0 obj /D (cite.SemEval2018Task1) endobj /Subtype /Link >> endobj /H /I 234 0 obj /A 22 0 R << It is interesting to note, that the common denominator for self-reported data are the three affective scores (arousal, valence, and dominance) collected on a discrete scale from 1 to 9. << /Border [ 0 0 1 ] /Descent -194 /Subtype /Link /A << /Subtype /Link /C [ 1 0 0 ] << /First 4 0 R >> >> /LastChar 20 /Resources 186 0 R >> >> /D [ 332 0 R /XYZ 170.431 552.373 null ] 265 0 obj For a given word and a dimension /Rect [ 51.161 363.411 110.922 374.354 ] /S /GoTo /D [ 91 0 R /XYZ 52.158 474.858 null ] /Type /Annot /ItalicAngle -14 endobj /QQAPF6907e3b5 418 0 R /Next 60 0 R endobj >> /Flags 4 endobj License required for commercial use. /Rect [ 303.87 492.237 456.278 503.181 ] endobj >> /Type /Annot /Subtype /Link /Names [ (page.10) 315 0 R (page.11) 325 0 R (page.12) 334 0 R (page.2) 153 0 R (page.3) Affective sciences are of burgeoning interest and are attracting more and more research attention. 14 0 obj endobj /A 78 0 R /F54 119 0 R /Next 84 0 R endobj >> /Prev 8 0 R /F61 124 0 R /Rect [ 161.669 443.328 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Adams and Giuseppe Destefanis and Daniel Graziotin and Marco Ortu}, … >> 321 0 obj Semantic structure was derived using the doc2vec algorithm, which classifies each text as a 300-place vector. /QQAPF6907e3b5 418 0 R /S /GoTo 141 0 obj /Next 48 0 R /Resources 176 0 R /CharSet (/A/B/C/D/E/F/G/I/K/L/M/N/O/P/Q/R/S/T/U/V/W/a/b/c/colon/comma/d/e/endash/f/fi/five/four/g/h/hyphen/i/k/l/m/n/numbersign/o/one/p/period/r/s/six/slash/t/three/two/u/v/w/x/y/zero) /CharSet (/eight/equal/four/one/parenleft/parenright/seven/two/zero) >> >> << /H /I /C [ 1 0 0 ] >> /C [ 1 0 0 ] /Type /Page >> /FontDescriptor 357 0 R /D [ 313 0 R /XYZ 52.158 167.244 null ] /S /GoTo /C [ 0 1 0 ] /A 62 0 R /Border [ 0 0 1 ] /Names [ (cite.frijda1988laws) 134 0 R (cite.kuder1937theory) 276 0 R (cite.maxdiff-naacl2016) >> >> endobj /S /GoTo /S /GoTo endobj << /H /I /Type /FontDescriptor >> /Subtype /Link 114 0 obj /Rect [ 86.952 165.44 93.926 174.366 ] >> [ 500 500 167 333 556 278 333 333 0 333 675 0 556 389 333 278 0 0 0 0 0 0 0 0 0 0 /Subtype /Type1 >> /C [ 1 0 0 ] /Type /Font >> endobj endobj /Border [ 0 0 1 ] endobj /C [ 1 0 0 ] /C [ 0 1 0 ] << << /C [ 0 1 0 ] We also present a detailed statistical and algorithmic analysis of the dataset … /CharSet (/A/B/C/D/E/F/G/H/I/J/K/L/M/N/O/P/Q/R/S/T/U/V/W/X/Y/Z/a/b/c/colon/comma/d/e/eight/emdash/endash/equal/f/fi/five/fl/four/g/h/hyphen/i/j/k/l/m/n/nine/numbersign/o/one/p/parenleft/parenright/percent/period/plus/q/question/quoteleft/quoteright/r/s/semicolon/seven/six/slash/t/three/two/u/v/w/x/y/z/zero) /Widths 338 0 R /S /GoTo >> /Subtype /Link >> EMOTIC Dataset. >> >> << /Subtype /Link /FontName /WECCLD+CMMI9 endobj /QQAPGScb572671 419 0 R /D (Hfootnote.5) /Prev 76 0 R /A 1 0 R >> endobj 170 0 obj /C [ 1 0 0 ] /QQAPF6907e3b5 418 0 R >> /D [ 187 0 R /XYZ 52.158 291.003 null ] /F56 120 0 R /Border [ 0 0 1 ] /Ascent 692 23 0 obj /Prev 12 0 R /D [ 209 0 R /XYZ 321.006 96.775 null ] endobj << /XHeight 431 /C [ 0 1 0 ] /Subtype /Link Our second contribution is the creation of a baseline system for the task of emotion recognition in context. /FirstChar 59 endobj endobj >> >> endobj endobj /A 70 0 R 81 0 obj 268 0 obj /C [ 0 1 0 ]
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