
# 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]
license:cc-by-nc-sa-4.0 region:us
{"license": ["cc-by-nc-sa-4.0"], "converted_from": "kaggle", "kaggle_id": "crowdflower/twitter-airline-sentiment"}
TAGS
#license-cc-by-nc-sa-4.0 #region-us 


# Dataset Card for Twitter US Airline Sentiment

## Table of Contents
- Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information
  - Contributions

## Dataset Description

- Homepage: URL
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:

### Dataset Summary

*This data originally came from Crowdflower's Data for Everyone library.*

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

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](URL

### Supported Tasks and Leaderboards



### Languages



## Dataset Structure

### Data Instances



### Data Fields



### Data Splits



## Dataset Creation

### Curation Rationale



### Source Data

#### Initial Data Collection and Normalization



#### Who are the source language producers?



### Annotations

#### Annotation process



#### Who are the annotators?



### Personal and Sensitive Information



## Considerations for Using the Data

### Social Impact of Dataset



### Discussion of Biases



### Other Known Limitations



## Additional Information

### Dataset Curators

This dataset was shared by @crowdflower

### Licensing Information

The license for this dataset is cc-by-nc-sa-4.0





### Contributions


# Dataset Card for Twitter US Airline Sentiment ## Table of Contents
- Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information
  - Contributions ## Dataset Description

- Homepage: URL
- Repository:
- Paper:
- Leaderboard:
- Point of Contact: ### Dataset Summary

*This data originally came from Crowdflower's Data for Everyone library.*

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

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](URL ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators

This dataset was shared by @crowdflower ### Licensing Information

The license for this dataset is cc-by-nc-sa-4.0 ### Contributions
TAGS
#license-cc-by-nc-sa-4.0 #region-us 
 # Dataset Card for Twitter US Airline Sentiment ## Table of Contents
- Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information
  - Contributions ## Dataset Description

- Homepage: URL
- Repository:
- Paper:
- Leaderboard:
- Point of Contact: ### Dataset Summary

*This data originally came from Crowdflower's Data for Everyone library.*

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

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](URL ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators

This dataset was shared by @crowdflower ### Licensing Information

The license for this dataset is cc-by-nc-sa-4.0 ### Contributions
passage: TAGS
#license-cc-by-nc-sa-4.0 #region-us 
# Dataset Card for Twitter US Airline Sentiment## Table of Contents
- Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information
  - Contributions## Dataset Description

- Homepage: URL
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:### Dataset Summary

*This data originally came from Crowdflower's Data for Everyone library.*

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

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](URL### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information