{"url":"/dataset/emocontext","name":"EmoContext","full_name":null,"description_markdown":"EmoContext consists of three-turn English Tweets. The emotion labels include happiness, sadness, anger and other.","description_withheld":null,"homepage":"https://competitions.codalab.org/competitions/19790","introduced_date":"2019-06-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/semeval-2019-task-3-emocontext-contextual","title":"SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text","first_author":"Ankush Chatterjee","url":null},"license":{"name":"Custom","url":"https://competitions.codalab.org/competitions/19790#learn_the_details-terms_and_conditions"},"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Emotion Recognition in Conversation","url":"/task/emotion-recognition-in-conversation","datasets_with_task":"/datasets/task/emotion-recognition-in-conversation"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["EC","EmoContext"],"data_loaders":[{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/SemEvalWorkshop/emo","frameworks":["tf","pytorch","jax"]},{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/shlomihod/emo-context","frameworks":["tf","pytorch","jax"]},{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/emo","frameworks":["tf","pytorch","jax"]}],"num_papers_in_archive":42,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/emotion-recognition-in-conversation-on-ec","task":"Emotion Recognition in Conversation","dataset_variant":"EC","rows":9,"metrics":["Micro-F1"],"first_row_in_archive_order":{"model":"NELEC","paper":"/paper/nelec-at-semeval-2019-task-3-think-twice","metrics":{"Micro-F1":"0.7765"},"code_links":[{"title":"iamgroot42/nelec","url":"https://github.com/iamgroot42/nelec"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/knowledge-enriched-transformer-for-emotion","title":"Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations","date":"2019-09-24","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/neural-feature-extraction-for-contextual","title":"Neural Feature Extraction for Contextual Emotion Detection","date":"2019-09-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/attention-based-modeling-for-emotion","title":"Attention-based Modeling for Emotion Detection and Classification in Textual Conversations","date":"2019-06-14","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/symantoresearch-at-semeval-2019-task-3","title":"SymantoResearch at SemEval-2019 Task 3: Combined Neural Models for Emotion Classification in Human-Chatbot Conversations","date":"2019-06-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/snu-ids-at-semeval-2019-task-3-addressing","title":"SNU IDS at SemEval-2019 Task 3: Addressing Training-Test Class Distribution Mismatch in Conversational Classification","date":"2019-06-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/conssed-at-semeval-2019-task-3-configurable","title":"ConSSED at SemEval-2019 Task 3: Configurable Semantic and Sentiment Emotion Detector","date":"2019-06-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/emotion-recognition-in-conversation-research","title":"Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances","date":"2019-05-08","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/nelec-at-semeval-2019-task-3-think-twice","title":"NELEC at SemEval-2019 Task 3: Think Twice Before Going Deep","date":"2019-04-05","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/ana-at-semeval-2019-task-3-contextual-emotion","title":"ANA at SemEval-2019 Task 3: Contextual Emotion detection in Conversations through hierarchical LSTMs and BERT","date":"2019-03-30","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":3,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}