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Register a new userNearest neighbour approaches for Emotion Detection in Tweets
أقرب نهج جار للكشف عن المشاعر في تغريدات
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كشف العاطفة مهمة مهمة يمكن تطبيقها على بيانات وسائل التواصل الاجتماعي لاكتشاف المعرفة الجديدة.في حين أن استخدام طرق التعلم العميق لهذه المهمة كان سائدا، فهي نماذج من الصندوق الأسود، مما يجعل قراراتها بجد لتفسير مشغل بشري.لذلك، في هذه الورقة، نقترح نهجا باستخدام Kevent Kearbors المرجح (KNN)، وهو نموذج تعليمي بسيط وسهل تنفيذي وشرحه.هذه الصفات يمكن أن تساعد في تعزيز موثوقية النتائج وتحليل الأخطاء التوجيه.على وجه الخصوص، نطبق نموذج KNN المرجح بمهمة الكشف عن العاطفة المشتركة في تغريدات Semeval-2018.يتم تمثيل التغريدات باستخدام أساليب مختلفة لتضمين نصية وعشرات المفردات المعجمية العاطفة، ويتم التصنيف من قبل مجموعة من نماذج KNN المرجحة.تتمتع أفضل أساليبنا بنتائج تنافسية مع حلول حديثة وفتح مسارا بديلا واعدا لأساليب الشبكة العصبية.
Emotion detection is an important task that can be applied to social media data to discover new knowledge. While the use of deep learning methods for this task has been prevalent, they are black-box models, making their decisions hard to interpret for a human operator. Therefore, in this paper, we propose an approach using weighted k Nearest Neighbours (kNN), a simple, easy to implement, and explainable machine learning model. These qualities can help to enhance results' reliability and guide error analysis. In particular, we apply the weighted kNN model to the shared emotion detection task in tweets from SemEval-2018. Tweets are represented using different text embedding methods and emotion lexicon vocabulary scores, and classification is done by an ensemble of weighted kNN models. Our best approaches obtain results competitive with state-of-the-art solutions and open up a promising alternative path to neural network methods.
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