Classification of Arabic Poetry Emotions Using Deep Learning

Abstract

The automatic classification of poems into various categories, such as by author or era, is an interesting problem. However, most current work categorizing Arabic poems into eras or emotions has utilized traditional feature engineering and machine learning approaches. This paper explores deep learning methods to classify Arabic poems into emotional categories. A new labeled poem emotion dataset was developed, containing 9452 poems with emotional labels of joy, sadness, and love. Various deep learning models were trained on this dataset. The results show that traditional deep learning models, such as one-dimensional Convolutional Neural Networks (1DCNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) networks, performed with F1-scores of 0.62, 0.62, and 0.53, respectively. However, the AraBERT model, an Arabic version of the Bidirectional Encoder Representations from Transformers (BERT), performed best, obtaining an accuracy of 76.5% and an F1-score of 0.77. This model outperformed the previous state-of-the-art in this domain.