JyotiNayak/political_ideologies

---
dataset_info:
features:
- name: statement
dtype: string
- name: label
dtype: int64
- name: issue_type
dtype: int64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 1138069
num_examples: 2560
- name: test
num_bytes: 141128
num_examples: 320
- name: validation
num_bytes: 145033
num_examples: 320
download_size: 699580
dataset_size: 1424230
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
license: apache-2.0
task_categories:
- text-classification
- question-answering
- zero-shot-classification
language:
- en
size_categories:
- 1K<n<10K
---

# Dataset Card for Dataset Name

<!-- Provide a quick summary of the dataset. -->

This dataset card contains very short paragraphs (2-3 sentences) which are labelled as either 'liberal' or 'conservative'. It has been generated using GPT-4.

## Dataset Details

### Dataset Description

The code to generate the data can be found here: https://github.com/jyotisn79/Labelled_data_generator

All the entries has also been manually checked to ensure that the paragraph accurately maps to the labels. Note that the lables may not be representative of political discourses outside of the United States.

Label Mapping: {'conservative': 0, 'liberal': 1}

Issue Type Mapping: {'economic': 0, 'environmental': 1, 'family/gender': 2, 'geo-political and foreign policy': 3, 'political': 4, 'racial justice and immigration': 5, 'religious': 6, 'social, health and education': 7}

- **Curated by:** Jyoti Shankar Nayak
- **Language(s) (NLP):** English
- **License:** Apache

### Dataset Sources [optional]

GPT-4

- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

<!-- Address questions around how the dataset is intended to be used. -->

### Direct Use

<!-- This section describes suitable use cases for the dataset. -->

This dataset can be a great starting point to train models to anaylyse political speeches and legal and political documents.

## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

[More Information Needed]

## Dataset Creation

### Curation Rationale

<!-- Motivation for the creation of this dataset. -->

[More Information Needed]

### Source Data

<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->

#### Data Collection and Processing

<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->

[More Information Needed]

#### Who are the source data producers?

<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->

[More Information Needed]

### Annotations [optional]

<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->

#### Annotation process

<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->

[More Information Needed]

#### Who are the annotators?

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[More Information Needed]

#### Personal and Sensitive Information

<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->

[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

## Citation [optional]

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

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**APA:**

[More Information Needed]

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->

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## More Information [optional]

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## Dataset Card Authors [optional]

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## Dataset Card Contact

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原始信息汇总

数据集卡片

数据集描述

数据集特征

• statement: 字符串类型
• label: 64位整数类型
• issue_type: 64位整数类型
• index_level_0: 64位整数类型

数据集分割

• train: 1138069字节，2560个样本
• test: 141128字节，320个样本
• validation: 145033字节，320个样本

数据集大小

• 下载大小: 699580字节
• 数据集大小: 1424230字节

配置

• default
  • train: data/train-*
  • test: data/test-*
  • validation: data/validation-*

许可证

• Apache 2.0

任务类别

• 文本分类
• 问答
• 零样本分类

语言

• 英语

数据集大小类别

• 1K<n<10K

数据集详情

标签映射

• conservative: 0
• liberal: 1

问题类型映射

• economic: 0
• environmental: 1
• family/gender: 2
• geo-political and foreign policy: 3
• political: 4
• racial justice and immigration: 5
• religious: 6
• social, health and education: 7

数据集来源

• GPT-4

语言（NLP）

• 英语

许可证

• Apache