languages:
- en
paperswithcode_id: null
pretty_name: YelpPolarity
train-eval-index:
- config: plain_text
task: text-classification
task_id: binary_classification
splits:
train_split: train
eval_split: test
col_mapping:
text: text
label: target
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 binary
args:
average: binary
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
Dataset Card for "yelp_polarity"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://course.fast.ai/datasets
- Repository: More Information Needed
- Paper: More Information Needed
- Point of Contact: More Information Needed
- Size of downloaded dataset files: 158.67 MB
- Size of the generated dataset: 421.28 MB
- Total amount of disk used: 579.95 MB
Dataset Summary
Large Yelp Review Dataset. This is a dataset for binary sentiment classification. We provide a set of 560,000 highly polar yelp reviews for training, and 38,000 for testing. ORIGIN The Yelp reviews dataset consists of reviews from Yelp. It is extracted from the Yelp Dataset Challenge 2015 data. For more information, please refer to http://www.yelp.com/dataset_challenge
The Yelp reviews polarity dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu) from the above dataset. It is first used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).
DESCRIPTION
The Yelp reviews polarity dataset is constructed by considering stars 1 and 2 negative, and 3 and 4 positive. For each polarity 280,000 training samples and 19,000 testing samples are take randomly. In total there are 560,000 trainig samples and 38,000 testing samples. Negative polarity is class 1, and positive class 2.
The files train.csv and test.csv contain all the training samples as comma-sparated values. There are 2 columns in them, corresponding to class index (1 and 2) and review text. The review texts are escaped using double quotes ("), and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is " ".
Supported Tasks and Leaderboards
Languages
Dataset Structure
Data Instances
plain_text
- Size of downloaded dataset files: 158.67 MB
- Size of the generated dataset: 421.28 MB
- Total amount of disk used: 579.95 MB
An example of 'train' looks as follows.
This example was too long and was cropped:
{
"label": 0,
"text": "\"Unfortunately, the frustration of being Dr. Goldberg's patient is a repeat of the experience I've had with so many other doctor..."
}
Data Fields
The data fields are the same among all splits.
plain_text
text: astringfeature.label: a classification label, with possible values including1(0),2(1).
Data Splits
| name | train | test |
|---|---|---|
| plain_text | 560000 | 38000 |
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
Licensing Information
Citation Information
@article{zhangCharacterlevelConvolutionalNetworks2015,
archivePrefix = {arXiv},
eprinttype = {arxiv},
eprint = {1509.01626},
primaryClass = {cs},
title = {Character-Level {{Convolutional Networks}} for {{Text Classification}}},
abstract = {This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several large-scale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results. Comparisons are offered against traditional models such as bag of words, n-grams and their TFIDF variants, and deep learning models such as word-based ConvNets and recurrent neural networks.},
journal = {arXiv:1509.01626 [cs]},
author = {Zhang, Xiang and Zhao, Junbo and LeCun, Yann},
month = sep,
year = {2015},
}
Contributions
Thanks to @patrickvonplaten, @lewtun, @mariamabarham, @thomwolf, @julien-c for adding this dataset.