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of 18
pro vyhledávání: '"Büchel, Sven"'
Autor:
Buechel, Sven, Hahn, Udo
Human emotion is expressed in many communication modalities and media formats and so their computational study is equally diversified into natural language processing, audio signal analysis, computer vision, etc. Similarly, the large variety of repre
Externí odkaz:
http://arxiv.org/abs/2308.07871
Autor:
Omitaomu, Damilola, Tafreshi, Shabnam, Liu, Tingting, Buechel, Sven, Callison-Burch, Chris, Eichstaedt, Johannes, Ungar, Lyle, Sedoc, João
Empathy is a cognitive and emotional reaction to an observed situation of others. Empathy has recently attracted interest because it has numerous applications in psychology and AI, but it is unclear how different forms of empathy (e.g., self-report v
Externí odkaz:
http://arxiv.org/abs/2205.12698
Autor:
Buechel, Sven, Hahn, Udo
We describe EmoBank, a corpus of 10k English sentences balancing multiple genres, which we annotated with dimensional emotion metadata in the Valence-Arousal-Dominance (VAD) representation format. EmoBank excels with a bi-perspectival and bi-represen
Externí odkaz:
http://arxiv.org/abs/2205.01996
Research in emotion analysis is scattered across different label formats (e.g., polarity types, basic emotion categories, and affective dimensions), linguistic levels (word vs. sentence vs. discourse), and, of course, (few well-resourced but much mor
Externí odkaz:
http://arxiv.org/abs/2012.00190
Emotion lexicons describe the affective meaning of words and thus constitute a centerpiece for advanced sentiment and emotion analysis. Yet, manually curated lexicons are only available for a handful of languages, leaving most languages of the world
Externí odkaz:
http://arxiv.org/abs/2005.05672
Publikováno v:
Proceedings of The 12th Language Resources and Evaluation Conference (LREC 2020). Pages 1657-1666
Despite the excellent performance of black box approaches to modeling sentiment and emotion, lexica (sets of informative words and associated weights) that characterize different emotions are indispensable to the NLP community because they allow for
Externí odkaz:
http://arxiv.org/abs/1912.01079
Publikováno v:
Proceedings of the Second Workshop on Economics and Natural Language Processing @ EMNLP 2019, Hong Kong, November 4, 2019, pages 16-21
We examine the affective content of central bank press statements using emotion analysis. Our focus is on two major international players, the European Central Bank (ECB) and the US Federal Reserve Bank (Fed), covering a time span from 1998 through 2
Externí odkaz:
http://arxiv.org/abs/1911.11522
One of the major downsides of Deep Learning is its supposed need for vast amounts of training data. As such, these techniques appear ill-suited for NLP areas where annotated data is limited, such as less-resourced languages or emotion analysis, with
Externí odkaz:
http://arxiv.org/abs/1810.10949
Computational detection and understanding of empathy is an important factor in advancing human-computer interaction. Yet to date, text-based empathy prediction has the following major limitations: It underestimates the psychological complexity of the
Externí odkaz:
http://arxiv.org/abs/1808.10399
We here introduce a substantially extended version of JeSemE, an interactive website for visually exploring computationally derived time-variant information on word meanings and lexical emotions assembled from five large diachronic text corpora. JeSe
Externí odkaz:
http://arxiv.org/abs/1807.04148