osanseviero/twitter-airline-sentiment

Name: osanseviero/twitter-airline-sentiment
Creator: osanseviero
Published: 2022-11-16 22:31:48
License: 暂无描述

Hugging Face2022-11-16 更新2024-03-04 收录

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--- license: - cc-by-nc-sa-4.0 converted_from: kaggle kaggle_id: crowdflower/twitter-airline-sentiment --- # Dataset Card for Twitter US Airline Sentiment ## Table of Contents - [Table of Contents](#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://kaggle.com/datasets/crowdflower/twitter-airline-sentiment - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary *This data originally came from [Crowdflower's Data for Everyone library](http://www.crowdflower.com/data-for-everyone).* As the original source says, > A sentiment analysis job about the problems of each major U.S. airline. Twitter data was scraped from February of 2015 and contributors were asked to first classify positive, negative, and neutral tweets, followed by categorizing negative reasons (such as "late flight" or "rude service"). The data we're providing on Kaggle is a slightly reformatted version of the original source. It includes both a CSV file and SQLite database. The code that does these transformations is [available on GitHub](https://github.com/benhamner/crowdflower-airline-twitter-sentiment) For example, it contains whether the sentiment of the tweets in this set was positive, neutral, or negative for six US airlines: [![airline sentiment graph](https://www.kaggle.io/svf/136065/a6e055ee6d877d2f7784dc42a15ecc43/airlineSentimentPlot.png)](https://www.kaggle.com/benhamner/d/crowdflower/twitter-airline-sentiment/exploring-airline-twitter-sentiment-data) ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators This dataset was shared by [@crowdflower](https://kaggle.com/crowdflower) ### Licensing Information The license for this dataset is cc-by-nc-sa-4.0 ### Citation Information ```bibtex [More Information Needed] ``` ### Contributions [More Information Needed]

--- 许可协议: - cc-by-nc-sa-4.0 转换来源: Kaggle Kaggle数据集ID: crowdflower/twitter-airline-sentiment --- # 美国航空公司推特情感数据集卡片 ## 目录 - [目录](#table-of-contents) - [数据集描述](#dataset-description) - [数据集概述](#dataset-summary) - [支持任务与排行榜](#supported-tasks-and-leaderboards) - [语言](#languages) - [数据集结构](#dataset-structure) - [数据实例](#data-instances) - [数据字段](#data-fields) - [数据划分](#data-splits) - [数据集构建](#dataset-creation) - [构建初衷](#curation-rationale) - [源数据](#source-data) - [标注](#annotations) - [个人与敏感信息](#personal-and-sensitive-information) - [数据集使用注意事项](#considerations-for-using-the-data) - [数据集的社会影响](#social-impact-of-dataset) - [偏差讨论](#discussion-of-biases) - [其他已知局限性](#other-known-limitations) - [附加信息](#additional-information) - [数据集维护者](#dataset-curators) - [许可信息](#licensing-information) - [引用信息](#citation-information) - [贡献](#contributions) ## 数据集描述 - **主页:** https://kaggle.com/datasets/crowdflower/twitter-airline-sentiment - **代码仓库:** - **相关论文:** - **排行榜:** - **联系人:** ### 数据集概述 *本数据集最初源自[Crowdflower的全民数据集库（Data for Everyone library）](http://www.crowdflower.com/data-for-everyone)。* 正如原始来源所述： > 这是一项针对美国各大航空公司运营问题的情感分析任务。数据集抓取了2015年2月的推特数据，要求参与者首先将推文划分为正面、负面与中性三类，随后对负面情感的原因进行细分标注（例如「航班延误」或「服务态度恶劣」）。我们在Kaggle平台上提供的数据集是原始版本经过小幅重构后的版本，包含CSV文件与SQLite数据库两种格式。用于完成格式转换的代码已[上传至GitHub](https://github.com/benhamner/crowdflower-airline-twitter-sentiment)。例如，本数据集包含针对6家美国航空公司的推文情感标签（正面、中性或负面）： [![航空情感分布统计图](https://www.kaggle.io/svf/136065/a6e055ee6d877d2f7784dc42a15ecc43/airlineSentimentPlot.png)](https://www.kaggle.com/benhamner/d/crowdflower/twitter-airline-sentiment/exploring-airline-twitter-sentiment-data) ### 支持任务与排行榜 [需补充更多信息] ### 语言 [需补充更多信息] ## 数据集结构 ### 数据实例 [需补充更多信息] ### 数据字段 [需补充更多信息] ### 数据划分 [需补充更多信息] ## 数据集构建 ### 构建初衷 [需补充更多信息] ### 源数据 #### 初始数据收集与标准化 [需补充更多信息] #### 源语言生产者是谁？ [需补充更多信息] ### 标注 #### 标注流程 [需补充更多信息] #### 标注人员是谁？ [需补充更多信息] ### 个人与敏感信息 [需补充更多信息] ## 数据集使用注意事项 ### 数据集的社会影响 [需补充更多信息] ### 偏差讨论 [需补充更多信息] ### 其他已知局限性 [需补充更多信息] ## 附加信息 ### 数据集维护者本数据集由[@Crowdflower](https://kaggle.com/crowdflower)分享 ### 许可信息本数据集的许可协议为CC BY-NC-SA 4.0 ### 引用信息 bibtex [需补充更多信息] ### 贡献 [需补充更多信息]

提供机构：

osanseviero

原始信息汇总

Twitter US Airline Sentiment 数据集概述

数据集描述

数据集总结

数据来源：Crowdflowers Data for Everyone library
数据内容：关于美国主要航空公司问题的情感分析数据，包括2015年2月收集的Twitter数据，涉及正面、负面和中性情绪分类，以及负面原因的分类（如“航班延误”或“服务态度差”）。
数据格式：包括CSV文件和SQLite数据库。