Datasets:
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
configs:
- default
- dictionary
- ptb
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
Dataset Card for sst
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
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
- 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 afloat
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 thedictionary
configuration. To obtain all the phrases in a sentence we need to visit the parse tree included with each example. In contrast, theptb
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 filmlabel
: the degree of "positivity" of the opinion, on a scale between 0.0 and 1.0tokens
: a sequence of tokens that form a sentencetree
: a sentence parse tree formatted as a parent pointer treephrase
: a sub-sentence of a complete sentenceptb_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 for adding this dataset.