arize-ai/beer_reviews_label_drift_neg

---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
pretty_name: sentiment-classification-reviews-with-drift
size_categories:
- 10K<n<100K
task_categories:
- text-classification
task_ids:
- sentiment-classification
---

# Dataset Card for `reviews_with_drift`

## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [language](#language)
- [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

### Dataset Summary

This dataset was crafted to be used in our tutorial [Link to the tutorial when ready]. It consists on a large Movie Review Dataset mixed with some reviews from a Hotel Review Dataset. The training/validation set are purely obtained from the Movie Review Dataset while the production set is mixed. Some other features have been added (`age`, `gender`, `context`) as well as a made up timestamp `prediction_ts` of when the inference took place.

### Supported Tasks and Leaderboards

`text-classification`, `sentiment-classification`: The dataset is mainly used for text classification: given the text, predict the sentiment (positive or negative).

### language

Text is mainly written in english.

## Dataset Structure

### Data Instances

[More Information Needed]

### Data Fields

[More Information Needed]

### Data Splits

[More Information Needed]

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

[More Information Needed]

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

[More Information Needed]

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

[More Information Needed]

### Citation Information

[More Information Needed]

### Contributions

Thanks to [@fjcasti1](https://github.com/fjcasti1) for adding this dataset.

原始信息汇总

数据集概述

• 名称: sentiment-classification-reviews-with-drift
• 别名: reviews_with_drift
• 语言: 英语 (en)
• 许可证: MIT
• 多语言性: 单语
• 大小: 10K<n<100K
• 任务类别: 文本分类
• 任务ID: 情感分类

数据集描述

数据集总结

• 该数据集用于教程，包含电影评论和酒店评论的混合数据。
• 训练/验证集来自电影评论数据集，生产集为混合。
• 添加了额外特征如age, gender, context和虚构的时间戳prediction_ts。

支持的任务和排行榜

• 主要用于文本分类任务，预测文本的情感倾向（正面或负面）。

数据集结构

数据实例

• [信息待补充]

数据字段

• [信息待补充]

数据分割

• [信息待补充]

数据集创建

数据来源

• [信息待补充]

初始数据收集和标准化

• [信息待补充]

源语言生产者

• [信息待补充]

注释

• 由专家生成

注释过程

• [信息待补充]

注释者

• [信息待补充]

个人和敏感信息

• [信息待补充]

使用数据的考虑

数据集的社会影响

• [信息待补充]

偏见讨论

• [信息待补充]

其他已知限制

• [信息待补充]

附加信息

数据集管理员

• [信息待补充]

许可信息

• MIT许可证

引用信息

• [信息待补充]

贡献

• 感谢@fjcasti1添加此数据集。