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BERT-CNN: A Deep Learning Model for Detecting Emotions from Text
Faculty
Computer Science
Year:
2021
Type of Publication:
ZU Hosted
Pages:
2943-2961
Authors:
Ahmed Raafat Abass Mohamed Saliem
Staff Zu Site
Abstract In Staff Site
Journal:
in CMC-Computers, Materials & Continua Tech Science Press
Volume:
71
Keywords :
BERT-CNN: , Deep Learning Model , Detecting Emotions
Abstract:
Due to the widespread usage of social media in our recent daily lifestyles, sentiment analysis becomes an important field in pattern recognition andNatural Language Processing (NLP). In this field, users’ feedback data on a specific issue are evaluated and analyzed.Detecting emotions within the text is therefore considered one of the important challenges of the current NLP research. Emotions have been widely studied in psychology and behavioral science as they are an integral part of the human nature. Emotions describe a state of mind of distinct behaviors, feelings, thoughts and experiences. The main objective of this paper is to propose a new model named BERT-CNN to detect emotions from text. This model is formed by a combination of the Bidirectional Encoder Representations from Transformer (BERT) and the Convolutional Neural networks (CNN) for textual classification. This model embraces the BERT to train the word semantic representation language model. According to the word context, the semantic vector is dynamically generated and then placed into the CNN to predict the output. Results of a comparative study proved that the BERT-CNN model overcomes the state-of-art baseline performance produced by different models in the literature using the semeval 2019 task3 dataset and ISEAR datasets. The BERTCNN model achieves an accuracy of 94.7% and an F1-score of 94% for semeval2019 task3 dataset and an accuracy of 75.8% and an F1-score of 76% for ISEAR dataset.
Author Related Publications
Ahmed Raafat Abass Mohamed Saliem, "Using General Regression with Local Tuning for Learning Mixture Models from Incomplete Data Sets", ScienceDirect, 2010
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Ahmed Raafat Abass Mohamed Saliem, "On determining efficient finite mixture models with compact and essential components for clustering data", ScienceDirect, 2013
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Ahmed Raafat Abass Mohamed Saliem, "Unsupervised learning of mixture models based on swarm intelligence and neural networks with optimal completion using incomplete data", ScienceDirect, 2012
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Ahmed Raafat Abass Mohamed Saliem, "Adaptive competitive learning neural networks", ScienceDirect, 2013
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Ahmed Raafat Abass Mohamed Saliem, "Using Incremental General Regression Neural Network for Learning Mixture Models from Incomplete Data", ScienceDirect, 2011
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Department Related Publications
Ahmed Salah Mohamed Mostafa, "Cluster-Distribute-Align-Merge: A General Algorithm to Speed Up Multiple Sequence Alignment on Multi-Core Computers", Journal of Computational and Theoretical Nanoscience, 2014
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Zaher Awad Aboelenieen Elhendy, "NEW APPROACH TO IMAGE EDGE DETECTION BASED ON QUANTUM ENTROPY", JOURNAL OF RUSSIAN LASER RESEARCH, 2016
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Sarah AbdelRazek Ahmed AbdulHameid, "Cloud Storage Forensics: Survey", International Journal of Engineering Trends and Technology (IJETT), 2017
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Doaa El-Shahat Barakat Mohammed, "A modified hybrid whale optimization algorithm for the scheduling problem in multimedia data objects", Wiley online library, 2019
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Mohammed Abdel Basset Metwally Attia, "A novel model for evaluation Hospital medical care systems based on plithogenic sets", Elsevier B.V., 2019
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