prova22303148/go_emotions

--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - apache-2.0 multilinguality: - monolingual size_categories: - 100K<n<1M - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - multi-class-classification - multi-label-classification paperswithcode_id: goemotions pretty_name: GoEmotions config_names: - raw - simplified tags: - emotion dataset_info: - config_name: raw features: - name: text dtype: string - name: id dtype: string - name: author dtype: string - name: subreddit dtype: string - name: link_id dtype: string - name: parent_id dtype: string - name: created_utc dtype: float32 - name: rater_id dtype: int32 - name: example_very_unclear dtype: bool - name: admiration dtype: int32 - name: amusement dtype: int32 - name: anger dtype: int32 - name: annoyance dtype: int32 - name: approval dtype: int32 - name: caring dtype: int32 - name: confusion dtype: int32 - name: curiosity dtype: int32 - name: desire dtype: int32 - name: disappointment dtype: int32 - name: disapproval dtype: int32 - name: disgust dtype: int32 - name: embarrassment dtype: int32 - name: excitement dtype: int32 - name: fear dtype: int32 - name: gratitude dtype: int32 - name: grief dtype: int32 - name: joy dtype: int32 - name: love dtype: int32 - name: nervousness dtype: int32 - name: optimism dtype: int32 - name: pride dtype: int32 - name: realization dtype: int32 - name: relief dtype: int32 - name: remorse dtype: int32 - name: sadness dtype: int32 - name: surprise dtype: int32 - name: neutral dtype: int32 splits: - name: train num_bytes: 55343102 num_examples: 211225 download_size: 24828322 dataset_size: 55343102 - config_name: simplified features: - name: text dtype: string - name: labels sequence: class_label: names: '0': admiration '1': amusement '2': anger '3': annoyance '4': approval '5': caring '6': confusion '7': curiosity '8': desire '9': disappointment '10': disapproval '11': disgust '12': embarrassment '13': excitement '14': fear '15': gratitude '16': grief '17': joy '18': love '19': nervousness '20': optimism '21': pride '22': realization '23': relief '24': remorse '25': sadness '26': surprise '27': neutral - name: id dtype: string splits: - name: train num_bytes: 4224138 num_examples: 43410 - name: validation num_bytes: 527119 num_examples: 5426 - name: test num_bytes: 524443 num_examples: 5427 download_size: 3464371 dataset_size: 5275700 configs: - config_name: raw data_files: - split: train path: raw/train-* - config_name: simplified data_files: - split: train path: simplified/train-* - split: validation path: simplified/validation-* - split: test path: simplified/test-* default: true --- # Dataset Card for GoEmotions ## 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/google-research/google-research/tree/master/goemotions - **Repository:** https://github.com/google-research/google-research/tree/master/goemotions - **Paper:** https://arxiv.org/abs/2005.00547 - **Leaderboard:** - **Point of Contact:** [Dora Demszky](https://nlp.stanford.edu/~ddemszky/index.html) ### Dataset Summary The GoEmotions dataset contains 58k carefully curated Reddit comments labeled for 27 emotion categories or Neutral. The raw data is included as well as the smaller, simplified version of the dataset with predefined train/val/test splits. ### Supported Tasks and Leaderboards This dataset is intended for multi-class, multi-label emotion classification. ### Languages The data is in English. ## Dataset Structure ### Data Instances Each instance is a reddit comment with a corresponding ID and one or more emotion annotations (or neutral). ### Data Fields The simplified configuration includes: - `text`: the reddit comment - `labels`: the emotion annotations - `comment_id`: unique identifier of the comment (can be used to look up the entry in the raw dataset) In addition to the above, the raw data includes: * `author`: The Reddit username of the comment's author. * `subreddit`: The subreddit that the comment belongs to. * `link_id`: The link id of the comment. * `parent_id`: The parent id of the comment. * `created_utc`: The timestamp of the comment. * `rater_id`: The unique id of the annotator. * `example_very_unclear`: Whether the annotator marked the example as being very unclear or difficult to label (in this case they did not choose any emotion labels). In the raw data, labels are listed as their own columns with binary 0/1 entries rather than a list of ids as in the simplified data. ### Data Splits The simplified data includes a set of train/val/test splits with 43,410, 5426, and 5427 examples respectively. ## Dataset Creation ### Curation Rationale From the paper abstract: > Understanding emotion expressed in language has a wide range of applications, from building empathetic chatbots to detecting harmful online behavior. Advancement in this area can be improved using large-scale datasets with a fine-grained typology, adaptable to multiple downstream tasks. ### Source Data #### Initial Data Collection and Normalization Data was collected from Reddit comments via a variety of automated methods discussed in 3.1 of the paper. #### Who are the source language producers? English-speaking Reddit users. ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? Annotations were produced by 3 English-speaking crowdworkers in India. ### Personal and Sensitive Information This dataset includes the original usernames of the Reddit users who posted each comment. Although Reddit usernames are typically disasociated from personal real-world identities, this is not always the case. It may therefore be possible to discover the identities of the individuals who created this content in some cases. ## Considerations for Using the Data ### Social Impact of Dataset Emotion detection is a worthwhile problem which can potentially lead to improvements such as better human/computer interaction. However, emotion detection algorithms (particularly in computer vision) have been abused in some cases to make erroneous inferences in human monitoring and assessment applications such as hiring decisions, insurance pricing, and student attentiveness (see [this article](https://www.unite.ai/ai-now-institute-warns-about-misuse-of-emotion-detection-software-and-other-ethical-issues/)). ### Discussion of Biases From the authors' github page: > Potential biases in the data include: Inherent biases in Reddit and user base biases, the offensive/vulgar word lists used for data filtering, inherent or unconscious bias in assessment of offensive identity labels, annotators were all native English speakers from India. All these likely affect labelling, precision, and recall for a trained model. Anyone using this dataset should be aware of these limitations of the dataset. ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Researchers at Amazon Alexa, Google Research, and Stanford. See the [author list](https://arxiv.org/abs/2005.00547). ### Licensing Information The GitHub repository which houses this dataset has an [Apache License 2.0](https://github.com/google-research/google-research/blob/master/LICENSE). ### Citation Information @inproceedings{demszky2020goemotions, author = {Demszky, Dorottya and Movshovitz-Attias, Dana and Ko, Jeongwoo and Cowen, Alan and Nemade, Gaurav and Ravi, Sujith}, booktitle = {58th Annual Meeting of the Association for Computational Linguistics (ACL)}, title = {{GoEmotions: A Dataset of Fine-Grained Emotions}}, year = {2020} } ### Contributions Thanks to [@joeddav](https://github.com/joeddav) for adding this dataset.### 数据集元数据 - 标注生成方式：众包标注（crowdsourced） - 数据来源语言：采集自现有公开文本（found） - 语言：英语（en） - 许可协议：Apache-2.0 - 多语言属性：单语言（monolingual） - 样本规模区间：10,000 < 样本数 < 100,000、100,000 < 样本数 < 1,000,000 - 源数据集：原始数据集（original） - 任务类别：文本分类（text-classification） - 任务子类别：多类别分类（multi-class-classification）、多标签分类（multi-label-classification） - PapersWithCode 标识符：goemotions - 友好名称：GoEmotions - 配置名称：原始版（raw）、简化版（simplified） - 标签：情感（emotion） #### 数据集信息（raw配置） - 特征字段： - `text`: 字符串类型，红迪（Reddit）评论内容 - `id`: 字符串类型，唯一标识符 - `author`: 字符串类型，评论作者用户名 - `subreddit`: 字符串类型，所属子红迪论坛（subreddit） - `link_id`: 字符串类型，链接ID - `parent_id`: 字符串类型，父级ID - `created_utc`: float32类型，UTC创建时间戳 - `rater_id`: int32类型，标注者ID - `example_very_unclear`: 布尔类型，是否标记示例标注难度极高 - 情感标签字段（均为int32类型，0/1取值）：钦佩（admiration）、愉悦（amusement）、愤怒（anger）、烦躁（annoyance）、认可（approval）、关怀（caring）、困惑（confusion）、好奇（curiosity）、渴望（desire）、失望（disappointment）、不认可（disapproval）、厌恶（disgust）、尴尬（embarrassment）、兴奋（excitement）、恐惧（fear）、感激（gratitude）、悲痛（grief）、喜悦（joy）、爱恋（love）、紧张（nervousness）、乐观（optimism）、自豪（pride）、领悟（realization）、释然（relief）、懊悔（remorse）、悲伤（sadness）、惊讶（surprise）、中性（neutral） - 数据划分：训练集，字节数：55343102，样本数：211225 - 下载大小：24828322，数据集总大小：55343102 #### 数据集信息（simplified配置） - 特征字段： - `text`: 字符串类型，红迪评论内容 - `labels`: 序列类型，类标签索引与名称对应：0: 钦佩，1: 愉悦，2: 愤怒，3: 烦躁，4: 认可，5: 关怀，6: 困惑，7: 好奇，8: 渴望，9: 失望，10: 不认可，11: 厌恶，12: 尴尬，13: 兴奋，14: 恐惧，15: 感激，16: 悲痛，17: 喜悦，18: 爱恋，19: 紧张，20: 乐观，21: 自豪，22: 领悟，23: 释然，24: 懊悔，25: 悲伤，26: 惊讶，27: 中性 - `id`: 字符串类型，唯一标识符 - 数据划分： - 训练集：字节数4224138，样本数43410 - 验证集：字节数527119，样本数5426 - 测试集：字节数524443，样本数5427 - 下载大小：3464371，数据集总大小：5275700 ### 配置项 - 配置名称：raw（原始版），数据文件： - 划分：训练集，路径：raw/train-* - 配置名称：simplified（简化版），数据文件： - 划分：训练集，路径：simplified/train-* - 划分：验证集，路径：simplified/validation-* - 划分：测试集，路径：simplified/test-* 默认启用配置：简化版 # GoEmotions 数据集卡片 ## 目录 - [数据集描述](#数据集描述) - [数据集概述](#数据集概述) - [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) - [数据实例](#数据实例) - [数据字段](#数据字段) - [数据划分](#数据划分) - [数据集构建](#数据集构建) - [筛选依据](#筛选依据) - [源数据](#源数据) - [标注](#标注) - [个人与敏感信息](#个人与敏感信息) - [数据使用注意事项](#数据使用注意事项) - [数据集的社会影响](#数据集的社会影响) - [偏见讨论](#偏见讨论) - [其他已知局限性](#其他已知局限性) - [附加信息](#附加信息) - [数据集整理者](#数据集整理者) - [许可信息](#许可信息) - [引用信息](#引用信息) - [贡献致谢](#贡献致谢) ## 数据集描述 - **主页:** https://github.com/google-research/google-research/tree/master/goemotions - **代码仓库:** https://github.com/google-research/google-research/tree/master/goemotions - **论文:** https://arxiv.org/abs/2005.00547 - **排行榜:** - **联系人:** [多萝蒂亚·德姆茨基（Dora Demszky）](https://nlp.stanford.edu/~ddemszky/index.html) ### 数据集概述 GoEmotions数据集包含5.8万条精心筛选的红迪评论，共标注了27种情感类别以及「中性」类别。数据集同时提供原始版本与更小体量的简化版本，其中简化版本已预定义训练/验证/测试划分。 ### 支持任务与排行榜 本数据集适用于多类别、多标签情感分类任务。 ### 语言 本数据集语言为英语。 ## 数据集结构 ### 数据实例 每条数据实例为一条红迪评论，附带对应ID以及一项或多项情感标注（或标注为中性）。 ### 数据字段 简化版配置包含以下字段： - `text`: 红迪评论内容 - `labels`: 情感标注序列，类标签索引与名称对应关系为：0: 钦佩，1: 愉悦，2: 愤怒，3: 烦躁，4: 认可，5: 关怀，6: 困惑，7: 好奇，8: 渴望，9: 失望，10: 不认可，11: 厌恶，12: 尴尬，13: 兴奋，14: 恐惧，15: 感激，16: 悲痛，17: 喜悦，18: 爱恋，19: 紧张，20: 乐观，21: 自豪，22: 领悟，23: 释然，24: 懊悔，25: 悲伤，26: 惊讶，27: 中性 - `id`: 评论的唯一标识符（可用于在原始数据集中检索对应条目） 除上述字段外，原始版本数据集还包含以下字段： * `author`: 评论作者的红迪用户名 * `subreddit`: 评论所属的子红迪论坛 * `link_id`: 评论的链接ID * `parent_id`: 评论的父级ID * `created_utc`: 评论的UTC创建时间戳 * `rater_id`: 标注者的唯一标识符 * `example_very_unclear`: 标注者是否标记该示例极难理解或标注（若标记为真，则未选择任何情感标签） 原始数据中的标签以单独的二进制0/1列形式呈现，而非简化版中的ID列表形式。 ### 数据划分 简化版本包含训练、验证、测试划分，样本数分别为43410、5426和5427。 ## 数据集构建 ### 筛选依据 摘自论文摘要： > 理解语言中表达的情感拥有广泛应用场景，从构建共情式聊天机器人到检测有害网络行为。使用具备细粒度分类体系且适配多种下游任务的大规模数据集，可推动该领域的研究进展。 ### 源数据 #### 初始数据采集与标准化 通过论文3.1节中提及的多种自动化方法，从红迪评论中采集得到原始数据并完成标准化处理。 #### 源语言生产者是谁？ 数据来自使用英语的红迪用户。 ### 标注 #### 标注流程 [需更多信息] #### 标注者是谁？ 标注工作由印度的3名英语母语众包标注者完成。 ### 个人与敏感信息 本数据集包含每条评论发布者的原始红迪用户名。尽管红迪用户名通常与现实个人身份无直接关联，但并非绝对如此。在部分场景下，仍有可能据此追溯到发布内容的个体身份。 ## 数据使用注意事项 ### 数据集的社会影响 情感检测是一项极具价值的研究课题，有望推动人机交互等领域的优化。然而，情感检测算法（尤其是计算机视觉方向）在部分场景中遭到滥用，例如在人员招聘、保险定价、学生专注力评估等人类监控与评估应用中做出错误推断（详见[此文章](https://www.unite.ai/ai-now-institute-warns-about-misuse-of-emotion-detection-software-and-other-ethical-issues/)）。 ### 偏见讨论 来自作者GitHub页面的说明：数据中潜在的偏见包括：红迪平台及其用户群体本身的固有偏见、数据过滤所用的冒犯性/粗俗词汇列表、对冒犯性身份标签评估中存在的固有或无意识偏见，以及所有标注者均为印度英语母语者。上述因素均可能影响模型训练时的标注精度与召回率。使用本数据集的人员应知晓该数据集的这些局限性。 ### 其他已知局限性 [需更多信息] ## 附加信息 ### 数据集整理者 本数据集由亚马逊Alexa、谷歌研究院（Google Research）以及斯坦福大学的研究人员整理维护。详见[作者列表](https://arxiv.org/abs/2005.00547)。 ### 许可信息 本数据集所在的GitHub仓库采用[Apache License 2.0](https://github.com/google-research/google-research/blob/master/LICENSE)协议。 ### 引用信息 bibtex @inproceedings{demszky2020goemotions, author = {Demszky, Dorottya and Movshovitz-Attias, Dana and Ko, Jeongwoo and Cowen, Alan and Nemade, Gaurav and Ravi, Sujith}, booktitle = {58th Annual Meeting of the Association for Computational Linguistics (ACL)}, title = {{GoEmotions: A Dataset of Fine-Grained Emotions}}, year = {2020} } ### 贡献致谢 感谢[@joeddav](https://github.com/joeddav)贡献本数据集。