Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
sentiment-classification
Languages:
English
Size:
1M - 10M
ArXiv:
License:
| annotations_creators: | |
| - crowdsourced | |
| language_creators: | |
| - crowdsourced | |
| language: | |
| - en | |
| license: | |
| - apache-2.0 | |
| multilinguality: | |
| - monolingual | |
| size_categories: | |
| - 1M<n<10M | |
| source_datasets: | |
| - original | |
| task_categories: | |
| - text-classification | |
| task_ids: | |
| - sentiment-classification | |
| paperswithcode_id: null | |
| pretty_name: Amazon Review Polarity | |
| train-eval-index: | |
| - config: amazon_polarity | |
| task: text-classification | |
| task_id: binary_classification | |
| splits: | |
| train_split: train | |
| eval_split: test | |
| col_mapping: | |
| content: text | |
| label: target | |
| metrics: | |
| - type: accuracy | |
| name: Accuracy | |
| - type: f1 | |
| name: F1 macro | |
| args: | |
| average: macro | |
| - type: f1 | |
| name: F1 micro | |
| args: | |
| average: micro | |
| - type: f1 | |
| name: F1 weighted | |
| args: | |
| average: weighted | |
| - 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_info: | |
| features: | |
| - name: label | |
| dtype: | |
| class_label: | |
| names: | |
| 0: negative | |
| 1: positive | |
| - name: title | |
| dtype: string | |
| - name: content | |
| dtype: string | |
| config_name: amazon_polarity | |
| splits: | |
| - name: train | |
| num_bytes: 1604364432 | |
| num_examples: 3600000 | |
| - name: test | |
| num_bytes: 178176193 | |
| num_examples: 400000 | |
| download_size: 688339454 | |
| dataset_size: 1782540625 | |
| # Dataset Card for Amazon Review Polarity | |
| ## 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://registry.opendata.aws/ | |
| - **Repository:** https://github.com/zhangxiangxiao/Crepe | |
| - **Paper:** https://arxiv.org/abs/1509.01626 | |
| - **Leaderboard:** [Needs More Information] | |
| - **Point of Contact:** [Xiang Zhang](mailto:xiang.zhang@nyu.edu) | |
| ### Dataset Summary | |
| The Amazon reviews dataset consists of reviews from amazon. | |
| The data span a period of 18 years, including ~35 million reviews up to March 2013. | |
| Reviews include product and user information, ratings, and a plaintext review. | |
| ### Supported Tasks and Leaderboards | |
| - `text-classification`, `sentiment-classification`: The dataset is mainly used for text classification: given the content and the title, predict the correct star rating. | |
| ### Languages | |
| Mainly English. | |
| ## Dataset Structure | |
| ### Data Instances | |
| A typical data point, comprises of a title, a content and the corresponding label. | |
| An example from the AmazonPolarity test set looks as follows: | |
| ``` | |
| { | |
| 'title':'Great CD', | |
| 'content':"My lovely Pat has one of the GREAT voices of her generation. I have listened to this CD for YEARS and I still LOVE IT. When I'm in a good mood it makes me feel better. A bad mood just evaporates like sugar in the rain. This CD just oozes LIFE. Vocals are jusat STUUNNING and lyrics just kill. One of life's hidden gems. This is a desert isle CD in my book. Why she never made it big is just beyond me. Everytime I play this, no matter black, white, young, old, male, female EVERYBODY says one thing ""Who was that singing ?""", | |
| 'label':1 | |
| } | |
| ``` | |
| ### Data Fields | |
| - 'title': a string containing the title of the review - 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 "\n". | |
| - 'content': a string containing the body of the document - 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 "\n". | |
| - 'label': either 1 (positive) or 0 (negative) rating. | |
| ### Data Splits | |
| The Amazon reviews polarity dataset is constructed by taking review score 1 and 2 as negative, and 4 and 5 as positive. Samples of score 3 is ignored. Each class has 1,800,000 training samples and 200,000 testing samples. | |
| ## Dataset Creation | |
| ### Curation Rationale | |
| The Amazon reviews polarity dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu). It is 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). | |
| ### Source Data | |
| #### Initial Data Collection and Normalization | |
| [Needs More Information] | |
| #### Who are the source language producers? | |
| [Needs More Information] | |
| ### 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 | |
| Apache License 2.0 | |
| ### Citation Information | |
| McAuley, Julian, and Jure Leskovec. "Hidden factors and hidden topics: understanding rating dimensions with review text." In Proceedings of the 7th ACM conference on Recommender systems, pp. 165-172. 2013. | |
| Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015) | |
| ### Contributions | |
| Thanks to [@hfawaz](https://github.com/hfawaz) for adding this dataset. |