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Register a new userEmoPars: A Collection of 30K Emotion-Annotated Persian Social Media Texts
Empars: مجموعة من نصوص التواصل الاجتماعي للشروح الاجتماعية للمشددين 30 كيلو
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مكنت الوصول الواسع من منصات وسائل التواصل الاجتماعي، مثل Twitter، العديد من المستخدمين من مشاركة أفكارهم وآرائهم وعواطفهم على مواضيع مختلفة عبر الإنترنت. سيسمح القدرة على الكشف عن هذه المشاعر تلقائيا العلماء الاجتماعيين، وكذلك الشركات التي يجب فهم الردود بشكل أفضل من الأمم والأزياء. في هذه الدراسة، نقدم مجموعة بيانات تتراوح بين 30،000 تغريدات فارسي تحمل مشاعر EKMAN الأساسية الستة (الغضب والخوف والسعادة والحزن والحزن والكراهية والعجب). هذه هي أول مجموعة بيانات العاطفة المتاحة للجمهور في اللغة الفارسية. في هذه الورقة، نوضح نظام جمع البيانات ووضع العلامات المستخدمة لإنشاء هذه البيانات. نقوم أيضا بتحليل مجموعة البيانات التي تم إنشاؤها، والتي تظهر ميزات وخصائص البيانات المختلفة. من بين أشياء أخرى، نحقق في حدوث مشاعر مختلفة في مجموعة البيانات، والعلاقة بين المعنويات والعاطفة الحالات النصية. تتوفر DataSet علنا في https://github.com/nazaninsbr/persian-emotion-detection.
The wide reach of social media platforms, such as Twitter, have enabled many users to share their thoughts, opinions and emotions on various topics online. The ability to detect these emotions automatically would allow social scientists, as well as, businesses to better understand responses from nations and costumers. In this study we introduce a dataset of 30,000 Persian Tweets labeled with Ekman's six basic emotions (Anger, Fear, Happiness, Sadness, Hatred, and Wonder). This is the first publicly available emotion dataset in the Persian language. In this paper, we explain the data collection and labeling scheme used for the creation of this dataset. We also analyze the created dataset, showing the different features and characteristics of the data. Among other things, we investigate co-occurrence of different emotions in the dataset, and the relationship between sentiment and emotion of textual instances. The dataset is publicly available at https://github.com/nazaninsbr/Persian-Emotion-Detection.
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