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Register a new userFrustration Level Annotation in Latvian Tweets with Non-Lexical Means of Expression
الشرح مستوى الإحباط في تغريدات لاتفيا مع وسائل التعبير غير المعجمية
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نقدم نموذجا مدفوعا بالشبكة العصبية للتعليق شدة الإحباط في تغريدات دعم العملاء، بناء على تمثيل نصوص تغريدة باستخدام ترميز حقيبة من الكلمات بعد المعالجة مع تجزئة الكلمات الفرعية مع الميزات غير المعجمية.تم تقييم النموذج على تغريدات اللغة الإنجليزية واللغة اللاتفية، مع التركيز على الجوانب التي تتجاوز تمثيلات الأكياس النقية المستخدمة في البحث السابق.تظهر النتائج التجريبية أن النموذج يمكن تطبيقه بنجاح للنصوص باللغة غير الإنجليزية، وأن إضافة ميزات غير معجمية لتغريد تمثيلات تعمل بشكل كبير، في حين أن تجزئة الكلمات الفرعية لها تأثير معتدل ولكن إيجابي على دقة النموذج.يتم توفير بيانات التعليمات البرمجية والتدريب علنا.
We present a neural-network-driven model for annotating frustration intensity in customer support tweets, based on representing tweet texts using a bag-of-words encoding after processing with subword segmentation together with non-lexical features. The model was evaluated on tweets in English and Latvian languages, focusing on aspects beyond the pure bag-of-words representations used in previous research. The experimental results show that the model can be successfully applied for texts in a non-English language, and that adding non-lexical features to tweet representations significantly improves performance, while subword segmentation has a moderate but positive effect on model accuracy. Our code and training data are publicly available.
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