EmoHinD: Fine-grained multilabel emotion recognition from Hindi texts with Deep learning

Shome, Debaditya

doi:10.1109/icccnt51525.2021.9579886

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2022

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“…We describe here the general guidelines we have used to create this dataset, readers can find details for each dataset in the Appendix. For all the datasets we removed the emotions that are not in the set joy, anger, fear, sadness (e.g., Cortiz et al (2021), Vasantharajan et al (2022), Shome (2021) used the 27 emotions from GoEmotion (Demszky et al, 2020) and we just collected the subset of our emotions). We have some exceptions to Twitter data, as the Tamil dataset Vasantharajan et al (2022) contains YouTube comments.…”

Section: Data and Related Workmentioning

confidence: 99%

XLM-EMO: Multilingual Emotion Prediction in Social Media Text

Bianchi¹,

Nozza²,

Hovy³

2022

Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment &Amp; Social Media Analysis

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Detecting emotion in text allows social and computational scientists to study how people behave and react to online events. However, developing these tools for different languages requires data that is not always available. This paper collects the available emotion detection datasets across 19 languages. We train a multilingual emotion prediction model for social media data, XLM-EMO. The model shows competitive performance in a zero-shot setting, suggesting it is helpful in the context of lowresource languages. We release our model to the community so that interested researchers can directly use it.

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Section: Data and Related Workmentioning

confidence: 99%