mitakashime/emotion

--- annotations_creators: - machine-generated language_creators: - machine-generated language: - en license: - other multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - multi-class-classification paperswithcode_id: emotion pretty_name: Emotion tags: - emotion-classification dataset_info: - config_name: split features: - name: text dtype: string - name: label dtype: class_label: names: '0': sadness '1': joy '2': love '3': anger '4': fear '5': surprise splits: - name: train num_bytes: 1741533 num_examples: 16000 - name: validation num_bytes: 214695 num_examples: 2000 - name: test num_bytes: 217173 num_examples: 2000 download_size: 1287193 dataset_size: 2173401 - config_name: unsplit features: - name: text dtype: string - name: label dtype: class_label: names: '0': sadness '1': joy '2': love '3': anger '4': fear '5': surprise splits: - name: train num_bytes: 45444017 num_examples: 416809 download_size: 26888538 dataset_size: 45444017 configs: - config_name: split data_files: - split: train path: split/train-* - split: validation path: split/validation-* - split: test path: split/test-* default: true - config_name: unsplit data_files: - split: train path: unsplit/train-* train-eval-index: - config: default task: text-classification task_id: multi_class_classification splits: train_split: train eval_split: test col_mapping: text: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- # Dataset Card for "emotion" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://github.com/dair-ai/emotion_dataset](https://github.com/dair-ai/emotion_dataset) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 16.13 MB - **Size of the generated dataset:** 47.62 MB - **Total amount of disk used:** 63.75 MB ### Dataset Summary 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. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances An example looks as follows. ``` { "text": "im feeling quite sad and sorry for myself but ill snap out of it soon", "label": 0 } ``` ### Data Fields The data fields are: - `text`: a `string` feature. - `label`: a classification label, with possible values including `sadness` (0), `joy` (1), `love` (2), `anger` (3), `fear` (4), `surprise` (5). ### Data Splits The dataset has 2 configurations: - split: with a total of 20_000 examples split into train, validation and split - unsplit: with a total of 416_809 examples in a single train split | name | train | validation | test | |---------|-------:|-----------:|-----:| | split | 16000 | 2000 | 2000 | | unsplit | 416809 | n/a | n/a | ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information The dataset should be used for educational and research purposes only. ### Citation Information If you use this dataset, please cite: ``` @inproceedings{saravia-etal-2018-carer, title = "{CARER}: Contextualized Affect Representations for Emotion Recognition", author = "Saravia, Elvis and Liu, Hsien-Chi Toby and Huang, Yen-Hao and Wu, Junlin and Chen, Yi-Shin", booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing", month = oct # "-" # nov, year = "2018", address = "Brussels, Belgium", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D18-1404", doi = "10.18653/v1/D18-1404", pages = "3687--3697", 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.", } ``` ### Contributions Thanks to [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun) for adding this dataset.annotations_creators: - 标注创建者：机器生成 language_creators: - 语言生成来源：机器生成 language: - 英语（en） license: - 其他许可证 multilinguality: - 单语言 size_categories: - 样本规模：10000 < 样本数 < 100000 source_datasets: - 原始数据集 task_categories: - 文本分类（text-classification） task_ids: - 多类别分类（multi-class-classification） paperswithcode_id: emotion pretty_name: Emotion tags: - 情感分类（emotion-classification） dataset_info: - config_name: split（拆分配置） features: - name: text dtype: 字符串 - name: label dtype: class_label: names: '0': 悲伤（sadness） '1': 喜悦（joy） '2': 喜爱（love） '3': 愤怒（anger） '4': 恐惧（fear） '5': 惊讶（surprise） splits: - name: 训练集 num_bytes: 1741533 num_examples: 16000 - name: 验证集 num_bytes: 214695 num_examples: 2000 - name: 测试集 num_bytes: 217173 num_examples: 2000 download_size: 1287193 dataset_size: 2173401 - config_name: unsplit（未拆分配置） features: - name: text dtype: 字符串 - name: label dtype: class_label: names: '0': 悲伤（sadness） '1': 喜悦（joy） '2': 喜爱（love） '3': 愤怒（anger） '4': 恐惧（fear） '5': 惊讶（surprise） splits: - name: 训练集 num_bytes: 45444017 num_examples: 416809 download_size: 26888538 dataset_size: 45444017 configs: - config_name: split（拆分配置） data_files: - split: 训练集 path: split/train-* - split: 验证集 path: split/validation-* - split: 测试集 path: split/test-* 默认配置: 是 - config_name: unsplit（未拆分配置） data_files: - split: 训练集 path: unsplit/train-* train-eval-index: - 配置: 默认配置 任务: 文本分类（text-classification） 任务子类别: 多类别分类（multi-class-classification） 拆分: 训练拆分: 训练集 评估拆分: 测试集 列映射: text: text label: target 评估指标: - 类型: 准确率（accuracy） 名称: Accuracy - 类型: F1宏平均（f1） 参数: average: 宏平均 名称: F1 macro - 类型: F1微平均（f1） 参数: average: 微平均 名称: F1 micro - 类型: F1加权平均（f1） 参数: average: 加权平均 名称: F1 weighted - 类型: 精确率宏平均（precision） 参数: average: 宏平均 名称: Precision macro - 类型: 精确率微平均（precision） 参数: average: 微平均 名称: Precision micro - 类型: 精确率加权平均（precision） 参数: average: 加权平均 名称: Precision weighted - 类型: 召回率宏平均（recall） 参数: average: 宏平均 名称: Recall macro - 类型: 召回率微平均（recall） 参数: average: 微平均 名称: Recall micro - 类型: 召回率加权平均（recall） 参数: average: 加权平均 名称: Recall weighted # 「Emotion」数据集卡片 ## 目录 - [数据集概述](#数据集概述) - [数据集摘要](#数据集摘要) - [支持任务与排行榜](#支持任务与排行榜) - [语言](#语言) - [数据集结构](#数据集结构) - [数据样例](#数据样例) - [数据字段](#数据字段) - [数据集拆分](#数据集拆分) - [数据集构建](#数据集构建) - [构建缘由](#构建缘由) - [源数据](#源数据) - [标注信息](#标注信息) - [个人与敏感信息](#个人与敏感信息) - [数据集使用注意事项](#数据集使用注意事项) - [数据集的社会影响](#数据集的社会影响) - [偏差讨论](#偏差讨论) - [其他已知局限](#其他已知局限) - [附加信息](#附加信息) - [数据集维护者](#数据集维护者) - [许可证信息](#许可证信息) - [引用信息](#引用信息) - [贡献者](#贡献者) ## 数据集概述 - **数据集主页**: [https://github.com/dair-ai/emotion_dataset](https://github.com/dair-ai/emotion_dataset) - **代码仓库**: [更多信息待补充](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **相关论文**: [更多信息待补充](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **联系人**: [更多信息待补充](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **下载数据集文件总大小**: 16.13 MB - **生成数据集总大小**: 47.62 MB - **总磁盘占用量**: 63.75 MB ### 数据集摘要 本数据集为英语推特文本数据集，涵盖六种基础情感：愤怒、恐惧、喜悦、喜爱、悲伤与惊讶。详细信息请参阅相关论文。 ### 支持任务与排行榜 [更多信息待补充](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### 语言 [更多信息待补充](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## 数据集结构 ### 数据样例 数据样例格式如下： { "text": "im feeling quite sad and sorry for myself but ill snap out of it soon", "label": 0 } ### 数据字段 数据集包含以下两个字段： - `text`: 字符串类型特征，用于存储输入文本 - `label`: 分类标签特征，可选取值及对应关系为：0对应悲伤（sadness）、1对应喜悦（joy）、2对应喜爱（love）、3对应愤怒（anger）、4对应恐惧（fear）、5对应惊讶（surprise） ### 数据集拆分 本数据集提供两种配置方案： 1. **split（拆分配置）**: 总计20000条样本，拆分为训练集、验证集与测试集 2. **unsplit（未拆分配置）**: 总计416809条样本，仅包含单一训练集拆分 | 配置名称 | 训练集样本数 | 验证集样本数 | 测试集样本数 | |---------|-------:|-----------:|-----:| | split | 16000 | 2000 | 2000 | | unsplit | 416809 | 无 | 无 | ## 数据集构建 ### 构建缘由 [更多信息待补充](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### 源数据 #### 初始数据收集与标准化 [更多信息待补充](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### 源语言生产者是谁？ [更多信息待补充](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### 标注信息 #### 标注流程 [更多信息待补充](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### 标注者是谁？ [更多信息待补充](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### 个人与敏感信息 [更多信息待补充](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## 数据集使用注意事项 ### 数据集的社会影响 [更多信息待补充](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### 偏差讨论 [更多信息待补充](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### 其他已知局限 [更多信息待补充](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## 附加信息 ### 数据集维护者 [更多信息待补充](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### 许可证信息 本数据集仅可用于教育与科研用途。 ### 引用信息 若您使用本数据集，请引用以下文献： @inproceedings{saravia-etal-2018-carer, title = "{CARER}: Contextualized Affect Representations for Emotion Recognition", author = "Saravia, Elvis and Liu, Hsien-Chi Toby and Huang, Yen-Hao and Wu, Junlin and Chen, Yi-Shin", booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing", month = oct # "-" # nov, year = "2018", address = "Brussels, Belgium", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D18-1404", doi = "10.18653/v1/D18-1404", pages = "3687--3697", abstract = "情感的表达具有细微差异，这会因群体或个体的经历、知识与信念而有所不同。因此，为了理解文本所传递的情感，我们需要一种能够捕捉并建模不同语言细微差别与语言现象的鲁棒机制。本文提出一种半监督的基于图的算法，以生成丰富的结构描述符，作为构建文本上下文情感表征的基础。该基于模式的表征进一步通过词嵌入进行增强，并通过多项情感识别任务进行评估。实验结果表明，所提方法在情感识别任务上优于当前主流技术。", } ### 贡献者 感谢[@lhoestq](https://github.com/lhoestq)、[@thomwolf](https://github.com/thomwolf)、[@lewtun](https://github.com/lewtun)为本数据集的添加提供的贡献。