hafsteinn/faroese_sentiment_analysis

---
license: cc-by-nc-4.0
task_categories:
- text-classification
language:
- fo
- en
tags:
- sentiment
- news
pretty_name: faroese sentiment dataset
size_categories:
- n<1K
configs:
- config_name: semicolon
data_files: "hf_dataset.csv"
sep: ";"
default: true
---

# Good or Bad News? Exploring GPT-4 for Sentiment Analysis on Faroese News Corpora

This dataset is a part of the research from the paper "Good or Bad News? Exploring GPT-4 for Sentiment Analysis for Faroese on a Public News Corpora," that focuses on the application of GPT-4 for sentiment analysis on Faroese news texts.

The study addresses the challenges of sentiment analysis in low-resource languages and evaluates the effectiveness of Large Language Models, specifically GPT-4, in understanding and analyzing sentiments in Faroese news articles.

## Dataset Description

The dataset comprises annotations of 225 sentences extracted from 170 Faroese news articles. The analysis was conducted at both the sentence and document levels, incorporating multi-class sentiment labels. The dataset features comparisons between GPT-4's performance and that of human annotators.

### Columns

- `News article`: The full text of the news article.
- `Selected Sentence`: The sentence selected for sentiment analysis.
- `Sentence label - GPT-4`: GPT-4's sentiment classification of the selected sentence.
- `Sentence label - Annotator 1`: The first human annotator's sentiment classification of the selected sentence.
- `Sentence label - Annotator 2`: The second human annotator's sentiment classification of the selected sentence.
- `News label - GPT-4`: GPT-4's sentiment classification of the entire news article.
- `News label - Annotator 1`: The first human annotator's sentiment classification of the entire news article.
- `News label - Annotator 2`: The second human annotator's sentiment classification of the entire news article.
- `Topic - GPT4`: GPT-4's classification of the article's topic.
- `Topic relevance - Annotator 1`: The first human annotator's assessment of the topic's relevance.
- `Correct topic if not relevant - Annotator 1`: The corrected topic by the first annotator if the original classification was deemed not relevant.
- `Topic (National (I) / International (I) / Mixed (M)) - Annotator 1`: The topic classification as National, International, or Mixed by the first human annotator.

## How to Cite

If you use this dataset for your research, please cite it as follows for now (will be updated once the proceedings have been formally published):

```
@inproceedings{debess2024goodbadnews,
title={Good or Bad News? Exploring GPT-4 for Sentiment Analysis for Faroese on a Public News Corpora},
author={Debess, Iben Nyholm and Simonsen, Annika and Einarsson, Hafsteinn},
booktitle={Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
year={2024}
}
```

原始信息汇总

数据集概述

该数据集是论文《好消息还是坏消息？探索GPT-4在公共新闻语料库上对法罗语进行情感分析的应用》研究的一部分，专注于使用GPT-4对法罗语新闻文本进行情感分析的应用。