A New Model for Emotions Analysis in Social Network Text Using Ensemble Learning and Deep learning
DOI:
https://doi.org/10.25007/ajnu.v11n1a1250الكلمات المفتاحية:
Emotion Analysis، Social Network، Ensemble Learning، Deep learningالملخص
Recently, emotion analysis has become widely used. Therefore, increasing the accuracy of existing methods has become a challenge for researchers. The proposed method in this paper is a hybrid model to improve the accuracy of emotion analysis; Which uses a combination of convolutional neural network and ensemble learning. In the proposed method, after receiving the dataset, the data is pre-processed and converted into process able samples. Then the new dataset is split into two categories of training and test. The proposed model is a structure for machine learning in the form of ensemble learning. It contains blocks consisting of a combination of convolutional networks and basic classification algorithms. In each convolutional network, the base classification algorithms replace the fully connected layer. Evaluate the proposed method, in IMDB, PL04 and SemEval dataset with accuracy, precision, recall and F1 criteria, shows that, on average, for all three datasets, the precision of polarity detection is 90%, the recall of polarity detection is 93%, the F1 of polarity detection is 91% and finally the accuracy of polarity detection is 92%.
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الحقوق الفكرية (c) 2022 Umran Abdullah Haje, Mohammed Hussein Abdalla, Reben Mohammed Saleem Kurda, Zhwan Mohammed Khalid
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المجلة الأكاديمية لجامعة نوروز ملتزمة بحماية خصوصية مستخدمي موقع المجلة هذا. سيتم استخدام الأسماء والتفاصيل الشخصية وعناوين البريد الإلكتروني التي تم إدخالها في هذا الموقع الإلكتروني فقط للأغراض المعلنة لهذه المجلة ولن يتم إتاحتها لأطراف ثالثة بدون إذن المستخدم أو الإجراءات القانونية الواجبة. موافقة المستخدمين مطلوبة لتلقي الاتصالات من المجلة الأكاديمية لجامعة نوروز للأغراض المعلنة للمجلة. ويمكن توجيه الاستفسارات المتعلقة بالخصوص إلى [email protected]