# Dataset: sst

Languages: en
Multilinguality: monolingual
Language Creators: found
Annotations Creators: crowdsourced

# Dataset Card for sst

### 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.

• 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

### Source Data

#### Who are the source language producers?

Rotten Tomatoes reviewers.

## Considerations for Using the Data

### 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",