TamilEmo：泰米尔语的细粒度情感检测数据集
TamilEmo: Finegrained Emotion Detection Dataset for Tamil
摘要：
从文本输入中进行情感分析一直被认为是自然语言处理中一项既有挑战性又有趣的任务。然而，由于缺乏低资源语言（如泰米尔语）的数据集，很难在这一领域进行高标准的研究。因此，我们引入了这个标记数据集（超过42k泰米尔语YouTube评论的最大手动注释数据集，标记了31种情绪，包括中性情绪）用于情绪识别。该数据集的目标是改善泰米尔语中多个下游任务的情感检测。我们还创建了三种不同的情绪分组（3类，7类和31类），并评估了模型在每个类别上的表现。我们的基于MURIL的模型在我们的3类组数据集中实现了0.60的宏观平均F1得分。对于7类和31类组，随机森林模型表现良好，宏观平均F1分数分别为0.42和0.29。
英文摘要：
Emotional Analysis from textual input has been considered both a challenging and interesting task in Natural Language Processing. However, due to the lack of datasets in low-resource languages (i.e. Tamil), it is difficult to conduct research of high standard in this area. Therefore we introduce this labelled dataset (a largest manually annotated dataset of more than 42k Tamil YouTube comments, labelled for 31 emotions including neutral) for emotion recognition. The goal of this dataset is to improve emotion detection in multiple downstream tasks in Tamil. We have also created three different groupings of our emotions (3-class, 7-class and 31-class) and evaluated the model's performance on each category of the grouping. Our MURIL-base model has achieved a 0.60 macro average F1-score across our 3-class group dataset. With 7-class and 31-class groups, the Random Forest model performed well with a macro average F1-scores of 0.42 and 0.29 respectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
