EZZELDIN, KHALED MOHAMED KHALED2025-02-272025-02-272024-0420160146https://bspace.buid.ac.ae/handle/1234/2807Dialect Speech Sentiment Analysis is an evolutional field where machine learning algorithms are utilized to detect emotions in spoken language. However, Arabic, particularly Egyptian Arabic, remains underrepresented, lacking a dedicated speech sentiment database. This thesis introduces a novel dataset specifically created for sentiment and emotion detection in the Egyptian Arabic dialect, generated from publicly available YouTube videos and annotated across seven emotional categories: anger, happiness, sadness, disgust, fear, romantic, and neutrality. The proposed solution involves leveraging a multi-stage machine learning pipeline that first extracts spectral features such as MFCC and mel spectrograms from acoustic speech waves using Fourier transformation. These features are then classified using a range of Deep Learning Models, including convolutional neural networks (CNN), bidirectional long-short-term memory (BI-LSTM), gated recurrent units (GRU), and Artificial Neural Networks (ANNs). A key contribution of this work is the development and evaluation of hybrid Deep Learning Models that combine CNN-BI-LSTM, CNN-GRU, GRU-CNN, GRU-BI-LSTM, and GRU-ANN architectures. The results demonstrate the superiority of the hybrid CNN-BI-LSTM model, achieving an accuracy of 93%, significantly outperforming individual deep-learning models such as CNN (87%) and BI-LSTM (83%). Additionally, the GRU-CNN hybrid model attained a notable accuracy of 90%. These findings establish the robustness and effectiveness of hybrid architectures in enhancing emotion recognition accuracy in Arabic speech data, presenting a novel approach for Arabic dialect sentiment analysis.ensentiment analysis, speech recognition, Arabic speech recognition, deep learning methods, hybrid deep learning approachesThesisProfessor Khaled Shaalan