000197185 001__ 197185
000197185 005__ 20181126150026.0
000197185 037__ $$aCONF
000197185 245__ $$aFine-Grained Emotion Recognition in Olympic Tweets Based on Human Computation
000197185 269__ $$a2013
000197185 260__ $$c2013
000197185 336__ $$aConference Papers
000197185 520__ $$aIn this paper, we detail a method for domain specific, multi-category emotion recognition, based on human computation. We create an Amazon Mechanical Turk1 task that elicits emotion labels and phrase-emotion associations from the participants. Using the proposed method, we create an emotion lexicon, compatible with the 20 emotion categories of the Geneva Emotion Wheel. GEW is the first computational resource that can be used to assign emotion labels with such a high level of granularity. Our emotion annotation method also produced a corpus of emotion labeled sports tweets. We compared the crossvalidated version of the lexicon with existing resources for both the positive/negative and multi-emotion classification problems. We show that the presented domain-targeted lexicon outperforms the existing general purpose ones in both settings. The performance gains are most pronounced for the fine-grained emotion classification, where we achieve an accuracy twice higher than the benchmark.2
000197185 700__ $$0246631$$aSintsova, Valentina$$g210754
000197185 700__ $$0245897$$aMusat, Claudiu-Cristian$$g220142
000197185 700__ $$0240575$$aPu, Pearl$$g106155
000197185 7112_ $$a4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis$$cAtlanta, Georgia
000197185 8564_ $$s239563$$uhttps://infoscience.epfl.ch/record/197185/files/W13-1603.pdf$$yn/a$$zn/a
000197185 909C0 $$0252184$$pLIA$$xU10406
000197185 909C0 $$0252148$$pHCI$$xU11056
000197185 909CO $$ooai:infoscience.tind.io:197185$$pconf$$pIC$$qGLOBAL_SET
000197185 917Z8 $$x208605
000197185 917Z8 $$x106155
000197185 937__ $$aEPFL-CONF-197185
000197185 973__ $$aEPFL$$rNON-REVIEWED$$sPUBLISHED
000197185 980__ $$aCONF