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SemEvalWorkshop/emo

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Tasks

text-classification

Citation text from the card

@inproceedings{chatterjee-etal-2019-semeval,
    title={SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text},
    author={Ankush Chatterjee and Kedhar Nath Narahari and Meghana Joshi and Puneet Agrawal},
    booktitle={Proceedings of the 13th International Workshop on Semantic Evaluation},
    year={2019},
    address={Minneapolis, Minnesota, USA},
    publisher={Association for Computational Linguistics},
    url={https://www.aclweb.org/anthology/S19-2005},
    doi={10.18653/v1/S19-2005},
    pages={39--48},
    abstract={In this paper, we present the SemEval-2019 Task 3 - EmoContext: Contextual Emotion Detection in Text. Lack of facial expressions and voice modulations make detecting emotions in text a challenging problem. For instance, as humans, on reading ''Why don't you ever text me!'' we can either interpret it as a sad or angry emotion and the same ambiguity exists for machines. However, the context of dialogue can prove helpful in detection of the emotion. In this task, given a textual dialogue i.e. an utterance along with two previous turns of context, the goal was to infer the underlying emotion of the utterance by choosing from four emotion classes - Happy, Sad, Angry and Others. To facilitate the participation in this task, textual dialogues from user interaction with a conversational agent were taken and annotated for emotion classes after several data processing steps. A training data set of 30160 dialogues, and two evaluation data sets, Test1

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dataset_licence({ "id": "SemEvalWorkshop/emo" })
signal: "unclear"
stated_licence: "unknown"
sources: 0