Amado at SemEval-2025 Task 11: Multi-label Emotion Detection inAmharic and English DataGirma Yohannis Bade, Olga Kolesnikova, José Luis OropezaGrigori Sidorov, Mesay Gemeda Yigezua(Centro de Investigaciones en Computación(CIC),Instituto Politécnico Nacional(IPN), Miguel Othon de Mendizabal,Ciudad de México, 07320, México.)

This research tackles the issue of fake news by utilizing the RNN-LSTM deep learning method with optimized hyperparameters identified through grid search. The model’s performance in multi-label classification is hindered by unbalanced data, despite its success in binary classification. We achieved a score of 0.82 in the binary classification task, whereas in the multi-class task, the score was 0.32. We suggest incorporating data balancing techniques for researchers who aim to further this task, aiming to improve results in managing a variety of information.