Robertuito Sentiment Analysis

🚀 西班牙语情感分析

robertuito-sentiment-analysis 是一个用于西班牙语情感分析的模型。它使用 TASS 2020 语料库（约 5000 条推文）进行训练，涵盖了多种西班牙语方言。该模型基于 RoBERTuito，这是一个在西班牙语推文中预训练的 RoBERTa 模型，使用 POS、NEG、NEU 标签进行情感分类。

🚀 快速开始

安装

你可以直接使用 pysentimiento 库来调用该模型。

使用示例

from pysentimiento import create_analyzer
analyzer = create_analyzer(task="sentiment", lang="es")

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

✨ 主要特性

• 多方言支持：使用涵盖多种西班牙语方言的 TASS 2020 语料库进行训练，能处理不同方言的文本。
• 预训练模型：基于在西班牙语推文中预训练的 RoBERTuito 模型，具有良好的语言理解能力。
• 标准标签：使用 POS、NEG、NEU 标准标签，便于进行情感分类。

📚 详细文档

模型仓库

模型仓库地址：https://github.com/pysentimiento/pysentimiento/

评估结果

以下是 pysentimiento 中四个任务的评估结果，结果以宏 F1 分数表示：

请注意，对于仇恨言论检测，这些是 Semeval 2019 任务 5 子任务 B 的结果。

📄 许可证

如果在你的研究中使用了该模型，请引用 pysentimiento、RoBERTuito 和 TASS 的相关论文：

@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}
}