Datasets:

dair-ai
/

emotion

{"emotion": {"description": "Emotion is a dataset of English Twitter messages with eight basic emotions: anger, anticipation,\ndisgust, fear, joy, sadness, surprise, and trust. For more detailed information please refer to the\npaper.\n", "citation": "@inproceedings{saravia-etal-2018-carer,\n title = \"{CARER}: Contextualized Affect Representations for Emotion Recognition\",\n author = \"Saravia, Elvis and\n Liu, Hsien-Chi Toby and\n Huang, Yen-Hao and\n Wu, Junlin and\n Chen, Yi-Shin\",\n booktitle = \"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing\",\n month = oct # \"-\" # nov,\n year = \"2018\",\n address = \"Brussels, Belgium\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/D18-1404\",\n doi = \"10.18653/v1/D18-1404\",\n pages = \"3687--3697\",\n abstract = \"Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semi-supervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks.\",\n}\n", "homepage": "https://github.com/dair-ai/emotion_dataset", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "emotion", "config_name": "emotion", "version": {"version_str": "0.1.0", "description": "First Emotion release", "datasets_version_to_prepare": null, "major": 0, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1754632, "num_examples": 16000, "dataset_name": "emotion"}, "validation": {"name": "validation", "num_bytes": 216248, "num_examples": 2000, "dataset_name": "emotion"}, "test": {"name": "test", "num_bytes": 218768, "num_examples": 2000, "dataset_name": "emotion"}}, "download_checksums": {"https://www.dropbox.com/s/1pzkadrvffbqw6o/train.txt?dl=1": {"num_bytes": 1658616, "checksum": "3ab03d945a6cb783d818ccd06dafd52d2ed8b4f62f0f85a09d7d11870865b190"}, "https://www.dropbox.com/s/2mzialpsgf9k5l3/val.txt?dl=1": {"num_bytes": 204240, "checksum": "34faaa31962fe63cdf5dbf6c132ef8ab166c640254ab991af78f3aea375e79ef"}, "https://www.dropbox.com/s/ikkqxfdbdec3fuj/test.txt?dl=1": {"num_bytes": 206760, "checksum": "60f531690d20127339e7f054edc299a82c627b5ec0dd5d552d53d544e0cfcc17"}}, "download_size": 2069616, "dataset_size": 2189648, "size_in_bytes": 4259264}, "default": {"description": "Emotion is a dataset of English Twitter messages with eight basic emotions: anger, anticipation,\ndisgust, fear, joy, sadness, surprise, and trust. For more detailed information please refer to the\npaper.\n", "citation": "@inproceedings{saravia-etal-2018-carer,\n title = \"{CARER}: Contextualized Affect Representations for Emotion Recognition\",\n author = \"Saravia, Elvis and\n Liu, Hsien-Chi Toby and\n Huang, Yen-Hao and\n Wu, Junlin and\n Chen, Yi-Shin\",\n booktitle = \"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing\",\n month = oct # \"-\" # nov,\n year = \"2018\",\n address = \"Brussels, Belgium\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/D18-1404\",\n doi = \"10.18653/v1/D18-1404\",\n pages = \"3687--3697\",\n abstract = \"Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semi-supervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks.\",\n}\n", "homepage": "https://github.com/dair-ai/emotion_dataset", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "emotion", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "datasets_version_to_prepare": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1754632, "num_examples": 16000, "dataset_name": "emotion"}, "validation": {"name": "validation", "num_bytes": 216248, "num_examples": 2000, "dataset_name": "emotion"}, "test": {"name": "test", "num_bytes": 218768, "num_examples": 2000, "dataset_name": "emotion"}}, "download_checksums": {"https://www.dropbox.com/s/1pzkadrvffbqw6o/train.txt?dl=1": {"num_bytes": 1658616, "checksum": "3ab03d945a6cb783d818ccd06dafd52d2ed8b4f62f0f85a09d7d11870865b190"}, "https://www.dropbox.com/s/2mzialpsgf9k5l3/val.txt?dl=1": {"num_bytes": 204240, "checksum": "34faaa31962fe63cdf5dbf6c132ef8ab166c640254ab991af78f3aea375e79ef"}, "https://www.dropbox.com/s/ikkqxfdbdec3fuj/test.txt?dl=1": {"num_bytes": 206760, "checksum": "60f531690d20127339e7f054edc299a82c627b5ec0dd5d552d53d544e0cfcc17"}}, "download_size": 2069616, "dataset_size": 2189648, "size_in_bytes": 4259264}}