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## Appendix 📚
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## Appendix 📚
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Please find an overview of the datasets used for training below. All datasets contain English text. The table summarizes which emotions are available in each of the datasets.
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Please find an overview of the datasets used for training below. All datasets contain English text. The table summarizes which emotions are available in each of the datasets.
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|Name|anger|disgust|fear|joy|neutral|sadness|surprise|
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|Name|anger|disgust|fear|joy|neutral|sadness|surprise|
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|MELD, Poria et al. (2019)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
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|MELD, Poria et al. (2019)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
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|SemEval-2018, EI-reg (Mohammad et al. 2018) |Yes|-|Yes|Yes|-|Yes|-|
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|SemEval-2018, EI-reg (Mohammad et al. 2018) |Yes|-|Yes|Yes|-|Yes|-|
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The datasets represent a diverse collection of text types. Specifically, they contain emotion labels for texts from Twitter, Reddit, student self-reports, and utterances from TV dialogues. As MELD (Multimodal EmotionLines Dataset) extends the popular EmotionLines dataset, EmotionLines itself is not included here.
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The datasets represent a diverse collection of text types. Specifically, they contain emotion labels for texts from Twitter, Reddit, student self-reports, and utterances from TV dialogues. As MELD (Multimodal EmotionLines Dataset) extends the popular EmotionLines dataset, EmotionLines itself is not included here.
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The model is trained on a balanced subset from the datasets listed above (2,811 observations per emotion, i.e., nearly 20k observations in total). The evaluation accuracy on a holdout test set is 66% (and significantly above the random-chance baseline of 1/7 = 14%).
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