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Register a new userModeling the Severity of Complaints in Social Media
نمذجة شدة الشكاوى في وسائل التواصل الاجتماعي
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يستخدم عمل خطاب الشكوى من قبل البشر للتواصل مع عدم وجود عدم تطابق سلبي بين الواقع والتوقعات كرد فعل على وضع غير موات. تصنف النظرية اللغوية للبراغماتية شكاوى إلى مستويات شدة مختلفة تعتمد على تهديد الوجه الذي يرغب فيه الشكوى في القيام به. هذا مفيد بشكل خاص لفهم نية الشكوكيين وكيف يطور البشر استراتيجيات اعتذار مناسبة. في هذه الورقة، ندرس مستوى شدة الشكاوى لأول مرة في اللغويات الحاسوبية. لتسهيل ذلك، فإننا نشعر بإثراء مجموعة بيانات متاحة للجمهور من الشكاوى مع أربع فئات شدة وتدريب شبكات مختلفة قائمة على المحولات جنبا إلى جنب مع المعلومات اللغوية التي تحقق 55.7 ماكرو F1. كما نقوم بالاشتراك في تصنيف تصنيف الشكاوى الثنائية وشدة الشكاوى في إعدادات متعددة المهام التي تحقق نتائج جديدة لتحقيق نتائج جديدة على اكتشاف الشكاوى الثنائية تصل إلى 88.2 ماكرو F1. أخيرا، نقدم تحليلا نوعيا لسلوك نماذجنا في التنبؤ بمستويات شدة الشكوى.
The speech act of complaining is used by humans to communicate a negative mismatch between reality and expectations as a reaction to an unfavorable situation. Linguistic theory of pragmatics categorizes complaints into various severity levels based on the face-threat that the complainer is willing to undertake. This is particularly useful for understanding the intent of complainers and how humans develop suitable apology strategies. In this paper, we study the severity level of complaints for the first time in computational linguistics. To facilitate this, we enrich a publicly available data set of complaints with four severity categories and train different transformer-based networks combined with linguistic information achieving 55.7 macro F1. We also jointly model binary complaint classification and complaint severity in a multi-task setting achieving new state-of-the-art results on binary complaint detection reaching up to 88.2 macro F1. Finally, we present a qualitative analysis of the behavior of our models in predicting complaint severity levels.
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