Datasets:

dair-ai
/

emotion

{"emotion":{"description":"Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. 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"}},"post_processed":null,"supervised_keys":null,"builder_name":"emotion","config_name":"emotion","version":{"version_str":"0.1.0","description":"First Emotion release","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,"post_processing_size":null,"dataset_size":2189648,"size_in_bytes":4259264},"default":{"description":"Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper.\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":{"num_classes":6,"names":["sadness","joy","love","anger","fear","surprise"],"names_file":null,"id":null,"_type":"ClassLabel"}},"post_processed":null,"supervised_keys":{"input":"text","output":"label"},"builder_name":"emotion","config_name":"default","version":{"version_str":"0.0.0","description":null,"major":0,"minor":0,"patch":0},"splits":{"train":{"name":"train","num_bytes":1741541,"num_examples":16000,"dataset_name":"emotion"},"validation":{"name":"validation","num_bytes":214699,"num_examples":2000,"dataset_name":"emotion"},"test":{"name":"test","num_bytes":217177,"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,"post_processing_size":null,"dataset_size":2173417,"size_in_bytes":4243033}}