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Register a new userOn the Usefulness of Personality Traits in Opinion-oriented Tasks
على فائدة سمات الشخصية في المهام الموجهة في الرأي
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نحن نستخدم محول ثنائي الاتجاه عميق لاستخراج نوع شخصية Myers-Briggs من البيانات التي تم إنشاؤها من قبل المستخدم في إعداد التصنيف متعدد العلامات ومتعددة الفئة.DataSet لدينا كبيرة وتكون من ثلاثة مجموعات بيانات شخصية متاحة من منصات وسائل التواصل الاجتماعي المختلفة بما في ذلك منتدى Reddit و Twitter و Personale Cafe.نقوم بتحفيز أشرطة الشخصية من نموذجنا القائم على المحولات والتحقيق في حالة استخدامها في مهام تصنيف النص المصب.تظهر الأدلة التجريبية أن تضمينات الشخصية فعالة في ثلاثة مهام التصنيف بما في ذلك التحقق من التأليف والمسابقات والكشف عن فرط النطاق.ونحن نقدم أيضا تحليلا جديدا وتفسيرا للمهمة الثالثة: تصنيف أخبار Hyperpartisan.
We use a deep bidirectional transformer to extract the Myers-Briggs personality type from user-generated data in a multi-label and multi-class classification setting. Our dataset is large and made up of three available personality datasets of various social media platforms including Reddit, Twitter, and Personality Cafe forum. We induce personality embeddings from our transformer-based model and investigate if they can be used for downstream text classification tasks. Experimental evidence shows that personality embeddings are effective in three classification tasks including authorship verification, stance, and hyperpartisan detection. We also provide novel and interpretable analysis for the third task: hyperpartisan news classification.
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