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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 5 of 5 1 Dystemo: Distant Supervision Method for Multi-Category Emotion Recognition in Tweets Sintsova, Valentina; Pu, Pearl In: http://infoscience.epfl.ch/record/223235 (2016) BASE Show details 2 Advancing Fine-Grained Emotion Recognition in Short Text Sintsova, Valentina In: http://infoscience.epfl.ch/record/221615 (2016) BASE Show details 3 Semi-Supervised Method for Multi-Category Emotion Recognition in Tweets Sintsova, Valentina; Musat, Claudiu-Cristian; Pu, Pearl In: http://infoscience.epfl.ch/record/210750 (2015) BASE Show details 4 EmotionWatch: Visualizing Fine-Grained Emotions in Event-Related Tweets Kempter, Renato; Sintsova, Valentina; Musat, Claudiu... In: Proceedings of the International AAAI Conference on Web and Social Media; Vol. 8 No. 1 (2014): Eighth International AAAI Conference on Weblogs and Social Media ; 2334-0770 ; 2162-3449 (2014) BASE Show details 5 Fine-Grained Emotion Recognition in Olympic Tweets Based on Human Computation Sintsova, Valentina; Musat, Claudiu-Cristian; Pu, Pearl In: http://infoscience.epfl.ch/record/197185 (2014) Abstract: In 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 URL: https://infoscience.epfl.ch/record/197185/files/W13-1603.pdf http://infoscience.epfl.ch/record/197185 BASE Hide details

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