osanseviero/twitter-airline-sentiment

---
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:

[](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]

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。

支持的任务和排行榜

• 信息待补充

语言

• 信息待补充

数据集结构

数据实例

• 信息待补充

数据字段

• 信息待补充

数据分割

• 信息待补充

数据集创建

数据收集理由

• 信息待补充

源数据

• 初始数据收集和标准化：信息待补充
• 源语言生产者：信息待补充

注释

• 注释过程：信息待补充
• 注释者：信息待补充

个人和敏感信息

• 信息待补充

使用数据的考虑

数据集的社会影响

• 信息待补充

偏见的讨论

• 信息待补充

其他已知限制

• 信息待补充

附加信息

数据集管理者

• 数据集由 @crowdflower 分享

许可信息

• 数据集许可证：cc-by-nc-sa-4.0

引用信息

• 信息待补充

贡献

• 信息待补充

搜集汇总

背景与挑战

背景概述

该数据集是一个用于情感分析的Twitter推文集合，专注于2015年美国航空公司的客户反馈。它包含约14,640条推文，每条推文标注了正面、负面或中性情感，并对负面推文进一步分类了原因（如航班延误、客户服务问题等），适用于自然语言处理和情感分析任务。