task_categories:text-classification task_ids:sentiment-classification license:cc-by-sa-4.0 region:us
Movie Review Dataset.

This is a dataset containing 4,265 positive and 4,265 negative processed
sentences from Rotten Tomatoes movie reviews.
@InProceedings{Pang+Lee:05a,
  author =       {Bo Pang and Lillian Lee},
  title =        {Seeing stars: Exploiting class relationships for sentiment
                  categorization with respect to rating scales},
  booktitle =    {Proceedings of the ACL},
  year =         2005
}
---
license: cc-by-sa-4.0
task_categories:
- text-classification
task_ids:
- sentiment-classification
---

# Dataset Card for "rotten_tomatoes_reviews"

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
- [Dataset Structure](#dataset-structure)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Additional Information](#additional-information)
  - [Citation Information](#citation-information)

## Dataset Description

- **Homepage:** [http://www.cs.cornell.edu/people/pabo/movie-review-data/](http://www.cs.cornell.edu/people/pabo/movie-review-data/)
- **Paper:** [https://arxiv.org/abs/cs/0506075](https://arxiv.org/abs/cs/0506075)

### Dataset Summary

Movie Review Dataset.

This is a dataset containing 4,265 positive and 4,265 negative processed
sentences from Rotten Tomatoes movie reviews.

## Dataset Structure

### Data Fields

The data fields are the same among all splits.

#### default
- `text`: a `string` feature.
- `label`: a classification label, with possible values including `neg` (0), `pos` (1).

### Data Splits

| name  |train|validation|test|
|-------|----:|---------:|---:|
|default| 8530|      1066|1066|

## Additional Information

### Citation Information

```
@InProceedings{Pang+Lee:05a,
  author =       {Bo Pang and Lillian Lee},
  title =        {Seeing stars: Exploiting class relationships for sentiment
                  categorization with respect to rating scales},
  booktitle =    {Proceedings of the ACL},
  year =         2005
}

```
