stanfordnlp/sst

---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scoring
- sentiment-classification
- sentiment-scoring
paperswithcode_id: sst
pretty_name: Stanford Sentiment Treebank
dataset_info:
- config_name: default
features:
- name: sentence
dtype: string
- name: label
dtype: float32
- name: tokens
dtype: string
- name: tree
dtype: string
splits:
- name: train
num_bytes: 2818768
num_examples: 8544
- name: validation
num_bytes: 366205
num_examples: 1101
- name: test
num_bytes: 730154
num_examples: 2210
download_size: 7162356
dataset_size: 3915127
- config_name: dictionary
features:
- name: phrase
dtype: string
- name: label
dtype: float32
splits:
- name: dictionary
num_bytes: 12121843
num_examples: 239232
download_size: 7162356
dataset_size: 12121843
- config_name: ptb
features:
- name: ptb_tree
dtype: string
splits:
- name: train
num_bytes: 2185694
num_examples: 8544
- name: validation
num_bytes: 284132
num_examples: 1101
- name: test
num_bytes: 566248
num_examples: 2210
download_size: 7162356
dataset_size: 3036074
config_names:
- default
- dictionary
- ptb
---

# Dataset Card for sst

## 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://nlp.stanford.edu/sentiment/index.html
- **Repository:** [Needs More Information]
- **Paper:** [Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank](https://www.aclweb.org/anthology/D13-1170/)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]

### Dataset Summary

The Stanford Sentiment Treebank is the first corpus with fully labeled parse trees that allows for a complete analysis of the compositional effects of sentiment in language.

### Supported Tasks and Leaderboards

- `sentiment-scoring`: Each complete sentence is annotated with a `float` label that indicates its level of positive sentiment from 0.0 to 1.0. One can decide to use only complete sentences or to include the contributions of the sub-sentences (aka phrases). The labels for each phrase are included in the `dictionary` configuration. To obtain all the phrases in a sentence we need to visit the parse tree included with each example. In contrast, the `ptb` configuration explicitly provides all the labelled parse trees in Penn Treebank format. Here the labels are binned in 5 bins from 0 to 4.
- `sentiment-classification`: We can transform the above into a binary sentiment classification task by rounding each label to 0 or 1.

### Languages

The text in the dataset is in English

## Dataset Structure

### Data Instances

For the `default` configuration:
```
{'label': 0.7222200036048889,
'sentence': 'Yet the act is still charming here .',
'tokens': 'Yet|the|act|is|still|charming|here|.',
'tree': '15|13|13|10|9|9|11|12|10|11|12|14|14|15|0'}
```

For the `dictionary` configuration:
```
{'label': 0.7361099720001221,
'phrase': 'still charming'}
```

For the `ptb` configuration:
```
{'ptb_tree': '(3 (2 Yet) (3 (2 (2 the) (2 act)) (3 (4 (3 (2 is) (3 (2 still) (4 charming))) (2 here)) (2 .))))'}
```

### Data Fields

- `sentence`: a complete sentence expressing an opinion about a film
- `label`: the degree of "positivity" of the opinion, on a scale between 0.0 and 1.0
- `tokens`: a sequence of tokens that form a sentence
- `tree`: a sentence parse tree formatted as a parent pointer tree
- `phrase`: a sub-sentence of a complete sentence
- `ptb_tree`: a sentence parse tree formatted in Penn Treebank-style, where each component's degree of positive sentiment is labelled on a scale from 0 to 4

### Data Splits

The set of complete sentences (both `default` and `ptb` configurations) is split into a training, validation and test set. The `dictionary` configuration has only one split as it is used for reference rather than for learning.

## Dataset Creation

### Curation Rationale

[Needs More Information]

### Source Data

#### Initial Data Collection and Normalization

[Needs More Information]

#### Who are the source language producers?

Rotten Tomatoes reviewers.

### Annotations

#### Annotation process

[Needs More Information]

#### Who are the annotators?

[Needs More Information]

### Personal and Sensitive Information

[Needs More Information]

## Considerations for Using the Data

### Social Impact of Dataset

[Needs More Information]

### Discussion of Biases

[Needs More Information]

### Other Known Limitations

[Needs More Information]

## Additional Information

### Dataset Curators

[Needs More Information]

### Licensing Information

[Needs More Information]

### Citation Information

```
@inproceedings{socher-etal-2013-recursive,
title = "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank",
author = "Socher, Richard and
Perelygin, Alex and
Wu, Jean and
Chuang, Jason and
Manning, Christopher D. and
Ng, Andrew and
Potts, Christopher",
booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2013",
address = "Seattle, Washington, USA",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D13-1170",
pages = "1631--1642",
}
```

### Contributions

Thanks to [@patpizio](https://github.com/patpizio) for adding this dataset.

原始信息汇总

数据集概述

数据集名称： Stanford Sentiment Treebank

数据集简述： 该数据集是首个带有完全标注解析树的语料库，用于分析语言中情感的组合效应。

支持的任务：

• 情感评分 (sentiment-scoring)：每个完整句子被标注一个从0.0到1.0的float标签，表示其正面情感的程度。
• 情感分类 (sentiment-classification)：通过将每个标签四舍五入为0或1，将上述任务转换为二元情感分类任务。

语言： 英语

数据集结构

数据实例：

• default 配置：包含句子、标签、词元和树结构。
• dictionary 配置：包含短语及其情感标签。
• ptb 配置：提供Penn Treebank格式的标注解析树。

数据字段：

• sentence：表达对电影意见的完整句子。
• label：意见的正面程度，范围从0.0到1.0。
• tokens：构成句子的词元序列。
• tree：句子的解析树，格式为父指针树。
• phrase：完整句子的子句。
• ptb_tree：Penn Treebank风格的句子解析树，每个组件的正面情感标签范围从0到4。

数据分割：

• default 和 ptb 配置：分为训练集、验证集和测试集。
• dictionary 配置：仅有一个分割，用于参考而非学习。

数据集创建

源数据生产者： Rotten Tomatoes评论者。

数据集贡献者： 感谢 @patpizio 添加此数据集。

引用信息

@inproceedings{socher-etal-2013-recursive, title = "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank", author = "Socher, Richard and Perelygin, Alex and Wu, Jean and Chuang, Jason and Manning, Christopher D. and Ng, Andrew and Potts, Christopher", booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing", month = oct, year = "2013", address = "Seattle, Washington, USA", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D13-1170", pages = "1631--1642", }