With the recent proliferation of open textual data on social media platforms, Emotion Detection (ED) from Text has received more attention over the past years. It has many applications, especially for businesses and online service providers, where emotion detection techniques can help them make informed commercial decisions by analyzing customers/users' feelings towards their products and services. In this study, we introduce ArmanEmo, a human-labeled emotion dataset of more than 7000 Persian sentences labeled for seven categories. The dataset has been collected from different resources, including Twitter, Instagram, and Digikala (an Iranian e-commerce company) comments. Labels are based on Ekman's six basic emotions (Anger, Fear, Happiness, Hatred, Sadness, Wonder) and another category (Other) to consider any other emotion not included in Ekman's model. Along with the dataset, we have provided several baseline models for emotion classification focusing on the state-of-the-art transformer-based language models. Our best model achieves a macro-averaged F1 score of 75.39 percent across our test dataset. Moreover, we also conduct transfer learning experiments to compare our proposed dataset's generalization against other Persian emotion datasets. Results of these experiments suggest that our dataset has superior generalizability among the existing Persian emotion datasets. ArmanEmo is publicly available for non-commercial use at https://github.com/Arman-Rayan-Sharif/arman-text-emotion.
翻译:随着社交媒体平台的公开文本数据最近扩散,Text的情感检测(ED)在过去几年中受到更多的关注。它有许多应用,特别是对于企业和在线服务提供商,情感检测技术可以通过分析客户/用户对其产品和服务的情感来帮助他们做出知情的商业决定。在这项研究中,我们介绍了ArmanEmo,这是7000多波斯语的人类标签情感数据集,标记为7000多波斯语的七个类别。数据集来自不同的资源,包括Twitter、Instagram和Digikala(伊朗电子商务公司)的评论。标签基于Ekman的六种基本情感(愤怒、恐惧、幸福、仇恨、悲观、奇观)和另一个类别(其他),以帮助他们通过分析Ekman模型中没有包含的任何其他情感。除了数据集之外,我们还提供了几个情感分类基线模型,重点是基于状态的变异语言模型。我们的最佳模型在测试数据集中取得了75.39 %的宏观平均F1分。此外,我们还在公开测试中将我们现有的亚鲁曼州/亚州级实验数据用于比较。我们提出的普通数据中的高级数据,我们现有的亚欧级数据是用于比较。