Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
csv
Languages:
Romanian
Size:
10K - 100K
License:
license: cc-by-sa-4.0 | |
task_categories: | |
- text-classification | |
language: | |
- ro | |
## Sentiment Analysis Data for the Romanian Language | |
**Dataset Description:** | |
This dataset contains a sentiment analysis dataset from Tache et al. (2021). | |
**Data Structure:** | |
The data was used for the project on [improving word embeddings with graph knowledge for Low Resource Languages](https://github.com/pyRis/retrofitting-embeddings-lrls?tab=readme-ov-file). | |
**Citation:** | |
```bibtex | |
@inproceedings{tache-etal-2021-clustering, | |
title = "Clustering Word Embeddings with Self-Organizing Maps. Application on {L}a{R}o{S}e{D}a - A Large {R}omanian Sentiment Data Set", | |
author = "Tache, Anca and | |
Mihaela, Gaman and | |
Ionescu, Radu Tudor", | |
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume", | |
month = apr, | |
year = "2021", | |
address = "Online", | |
publisher = "Association for Computational Linguistics", | |
url = "https://www.aclweb.org/anthology/2021.eacl-main.81", | |
pages = "949--956", | |
abstract = "Romanian is one of the understudied languages in computational linguistics, with few resources available for the development of natural language processing tools. In this paper, we introduce LaRoSeDa, a Large Romanian Sentiment Data Set, which is composed of 15,000 positive and negative reviews collected from the largest Romanian e-commerce platform. We employ two sentiment classification methods as baselines for our new data set, one based on low-level features (character n-grams) and one based on high-level features (bag-of-word-embeddings generated by clustering word embeddings with k-means). As an additional contribution, we replace the k-means clustering algorithm with self-organizing maps (SOMs), obtaining better results because the generated clusters of word embeddings are closer to the Zipf{'}s law distribution, which is known to govern natural language. We also demonstrate the generalization capacity of using SOMs for the clustering of word embeddings on another recently-introduced Romanian data set, for text categorization by topic.", | |
} | |
``` |