Deep Learning-Based Sentiment Analysis of Twitter Discourse on the Gaza and Ukraine Conflicts Using Bi-GRU Architecture - Garcia Krisnando Nathanael; Rizal Akbar Aldyan; Tran Minh Hop; Imelda Masni Juniaty Sianipar; Dairatul Maarif; Zayyin Abdul Quddus (Universitas Muhammadiyah Tangerang, 2025)
The proliferation of social media has transformed platforms like Twitter into dynamic arenas for expressing public sentiment during geopolitical crises. This study examines global public opinion on the Gaza and Ukraine conflicts by employing a deep learning-based sentiment analysis model utilizing a Bidirectional Gated Recurrent Unit (Bi-GRU) architecture. A total of 24,177 tweets were collected and pre-processed, followed by sentiment labeling using a hybrid lexical approach that combines VADER and TextBlob. Feature extraction was conducted using the TF-IDF method, and the Bi-GRU model was trained and evaluated using standard performance metrics. The model achieved an accuracy of 88.06% and an average F1-score of 85.07%, demonstrating superior performance in recognizing sentiment, especially for negative expressions. Word cloud analysis further revealed the dominance of emotionally charged terms such as