asparius/Georgian-Sentiment

# Georgian Sentiment Analysis Dataset

This data is orinally from https://aclanthology.org/2022.lrec-1.173
## BibTeX Citation

If you use this dataset, please cite following paper:

```
@inproceedings{stefanovitch-etal-2022-resources,
title = "Resources and Experiments on Sentiment Classification for {G}eorgian",
author = "Stefanovitch, Nicolas and
Piskorski, Jakub and
Kharazi, Sopho",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.173",
pages = "1613--1621",
abstract = "This paper presents, to the best of our knowledge, the first ever publicly available annotated dataset for sentiment classification and semantic polarity dictionary for Georgian. The characteristics of these resources and the process of their creation are described in detail. The results of various experiments on the performance of both lexicon- and machine learning-based models for Georgian sentiment classification are also reported. Both 3-label (positive, neutral, negative) and 4-label settings (same labels + mixed) are considered. The machine learning models explored include, i.a., logistic regression, SVMs, and transformed-based models. We also explore transfer learning- and translation-based (to a well-supported language) approaches. The obtained results for Georgian are on par with the state-of-the-art results in sentiment classification for well studied languages when using training data of comparable size.",
}

```

---
license: cc-by-4.0
---

原始信息汇总

Georgian Sentiment Analysis Dataset

数据集来源

• 原始来源：https://aclanthology.org/2022.lrec-1.173

引用信息

• 标题：Resources and Experiments on Sentiment Classification for Georgian
• 作者：Stefanovitch, Nicolas; Piskorski, Jakub; Kharazi, Sopho
• 编辑：Calzolari, Nicoletta 等
• 出版物：Proceedings of the Thirteenth Language Resources and Evaluation Conference
• 日期：2022年6月
• 出版者：European Language Resources Association
• 页码：1613--1621
• 摘要：本论文介绍了首个公开可用的格鲁吉亚语情感分类和语义极性词典的标注数据集。详细描述了这些资源的特性和创建过程，并报告了基于词汇和机器学习的格鲁吉亚语情感分类模型的各种实验结果。考虑了3标签（积极、中性、消极）和4标签设置（相同标签+混合）。探索的机器学习模型包括逻辑回归、SVM和支持向量机等。还探讨了基于迁移学习和翻译（到支持良好的语言）的方法。使用可比大小的训练数据时，格鲁吉亚语的获得结果与情感分类的最新技术水平相当。

许可证

• 许可证：CC-BY-4.0