climatebert/climate_sentiment

---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license: cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: ClimateSentiment
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': risk
'1': neutral
'2': opportunity
splits:
- name: train
num_bytes: 492077
num_examples: 1000
- name: test
num_bytes: 174265
num_examples: 320
download_size: 373638
dataset_size: 666342
---

# Dataset Card for climate_sentiment

## Dataset Description

- **Homepage:** [climatebert.ai](https://climatebert.ai)
- **Repository:**
- **Paper:** [papers.ssrn.com/sol3/papers.cfm?abstract_id=3998435](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3998435)
- **Leaderboard:**
- **Point of Contact:** [Nicolas Webersinke](mailto:[e-mail])

### Dataset Summary

We introduce an expert-annotated dataset for classifying climate-related sentiment of climate-related paragraphs in corporate disclosures.

### Supported Tasks and Leaderboards

The dataset supports a ternary sentiment classification task of whether a given climate-related paragraph has sentiment opportunity, neutral, or risk.

### Languages

The text in the dataset is in English.

## Dataset Structure

### Data Instances

```
{
'text': '− Scope 3: Optional scope that includes indirect emissions associated with the goods and services supply chain produced outside the organization. Included are emissions from the transport of products from our logistics centres to stores (downstream) performed by external logistics operators (air, land and sea transport) as well as the emissions associated with electricity consumption in franchise stores.',
'label': 1
}
```

### Data Fields

- text: a climate-related paragraph extracted from corporate annual reports and sustainability reports
- label: the label (0 -> risk, 1 -> neutral, 2 -> opportunity)

### Data Splits

The dataset is split into:
- train: 1,000
- test: 320

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

Our dataset contains climate-related paragraphs extracted from financial disclosures by firms. We collect text from corporate annual reports and sustainability reports.

For more information regarding our sample selection, please refer to the Appendix of our paper (see [citation](#citation-information)).

#### Who are the source language producers?

Mainly large listed companies.

### Annotations

#### Annotation process

For more information on our annotation process and annotation guidelines, please refer to the Appendix of our paper (see [citation](#citation-information)).

#### Who are the annotators?

The authors and students at Universität Zürich and Friedrich-Alexander-Universität Erlangen-Nürnberg with majors in finance and sustainable finance.

### Personal and Sensitive Information

Since our text sources contain public information, no personal and sensitive information should be included.

## 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

- Julia Anna Bingler
- Mathias Kraus
- Markus Leippold
- Nicolas Webersinke

### Licensing Information

This dataset is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license (cc-by-nc-sa-4.0). To view a copy of this license, visit [creativecommons.org/licenses/by-nc-sa/4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/).

If you are interested in commercial use of the dataset, please contact [[e-mail]](mailto:[e-mail]).

### Citation Information

```bibtex
@techreport{bingler2023cheaptalk,
title={How Cheap Talk in Climate Disclosures Relates to Climate Initiatives, Corporate Emissions, and Reputation Risk},
author={Bingler, Julia and Kraus, Mathias and Leippold, Markus and Webersinke, Nicolas},
type={Working paper},
institution={Available at SSRN 3998435},
year={2023}
}
```

### Contributions

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

原始信息汇总

数据集概述

数据集名称

• 名称: ClimateSentiment

数据集描述

• 任务类型: 文本分类
• 任务详情: 三元情感分类任务，判断气候相关段落的情感倾向为机会、中性或风险。
• 语言: 英语

数据集结构

• 数据字段:
  • text: 字符串类型，来自企业年报和可持续发展报告的气候相关段落。
  • label: 类别标签，0表示风险，1表示中性，2表示机会。
• 数据分割:
  • 训练集: 1000个样本
  • 测试集: 320个样本

数据集创建

• 来源数据: 来自大型上市公司的财务披露，包括企业年报和可持续发展报告。
• 注释者: 来自苏黎世大学和埃尔朗根-纽伦堡大学的金融和可持续金融专业的作者及学生。

许可信息

• 许可证: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (cc-by-nc-sa-4.0)

数据集大小

• 下载大小: 373638字节
• 数据集大小: 666342字节
• 样本数量: 训练集1000个，测试集320个

数据集特征

• 特征:
  • text: 字符串
  • label: 类别标签，包含三个类别：风险、中性和机会。