Robertuito Sentiment Analysis

🚀 スペイン語によるセンチメント分析

このモデルは、いくつかのスペイン語方言のTASS 2020コーパス（約5,000件のツイート）を使用して学習されました。ベースモデルは、スペイン語のツイートで学習されたRoBERTaモデルであるRoBERTuitoです。POS、NEG、NEUのラベルを使用します。

🚀 クイックスタート

リポジトリ: https://github.com/pysentimiento/pysentimiento/

✨ 主な機能

• スペイン語のセンチメント分析を行うことができます。
• POS（肯定的）、NEG（否定的）、NEU（中立的）のラベルを使用して分類を行います。

💻 使用例

基本的な使用法

# 元のコードとコメントを保持
from pysentimiento import create_analyzer
analyzer = create_analyzer(task="sentiment", lang="es")

analyzer.predict("Qué gran jugador es Messi")
# returns AnalyzerOutput(output=POS, probas={POS: 0.998, NEG: 0.002, NEU: 0.000})

📚 ドキュメント

結果

pysentimientoで評価された4つのタスクの結果です。結果はマクロF1スコアで表されています。

なお、ヘイトスピーチに関しては、Semeval 2019、タスク5サブタスクBの結果です。

📄 ライセンス

もしこのモデルをあなたの研究で使用する場合は、pysentimiento、RoBERTuito、TASSの論文を引用してください。

@article{perez2021pysentimiento,
  title={pysentimiento: a python toolkit for opinion mining and social NLP tasks},
  author={P{\'e}rez, Juan Manuel and Rajngewerc, Mariela and Giudici, Juan Carlos and Furman, Dami{\'a}n A and Luque, Franco and Alemany, Laura Alonso and Mart{\'\i}nez, Mar{\'\i}a Vanina},
  journal={arXiv preprint arXiv:2106.09462},
  year={2021}
}

@inproceedings{perez-etal-2022-robertuito,
    title = "{R}o{BERT}uito: a pre-trained language model for social media text in {S}panish",
    author = "P{\'e}rez, Juan Manuel  and
      Furman, Dami{\'a}n Ariel  and
      Alonso Alemany, Laura  and
      Luque, Franco M.",
    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-1.785",
    pages = "7235--7243",
    abstract = "Since BERT appeared, Transformer language models and transfer learning have become state-of-the-art for natural language processing tasks. Recently, some works geared towards pre-training specially-crafted models for particular domains, such as scientific papers, medical documents, user-generated texts, among others. These domain-specific models have been shown to improve performance significantly in most tasks; however, for languages other than English, such models are not widely available. In this work, we present RoBERTuito, a pre-trained language model for user-generated text in Spanish, trained on over 500 million tweets. Experiments on a benchmark of tasks involving user-generated text showed that RoBERTuito outperformed other pre-trained language models in Spanish. In addition to this, our model has some cross-lingual abilities, achieving top results for English-Spanish tasks of the Linguistic Code-Switching Evaluation benchmark (LinCE) and also competitive performance against monolingual models in English Twitter tasks. To facilitate further research, we make RoBERTuito publicly available at the HuggingFace model hub together with the dataset used to pre-train it.",
}

@inproceedings{garcia2020overview,
  title={Overview of TASS 2020: Introducing emotion detection},
  author={Garc{\'\i}a-Vega, Manuel and D{\'\i}az-Galiano, MC and Garc{\'\i}a-Cumbreras, MA and Del Arco, FMP and Montejo-R{\'a}ez, A and Jim{\'e}nez-Zafra, SM and Mart{\'\i}nez C{\'a}mara, E and Aguilar, CA and Cabezudo, MAS and Chiruzzo, L and others},
  booktitle={Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2020) Co-Located with 36th Conference of the Spanish Society for Natural Language Processing (SEPLN 2020), M{\'a}laga, Spain},
  pages={163--170},
  year={2020}
}