Description:
More than any other literary genre, poetry presents a significant challenge for Natural Language Processing (NLP) algorithms. Small poetries in the Persian language are called ghazal. Ghazal classification by document embedding technique and sequential learning on poetic era is an under-explored area of research till now. Deep learning and document embedding technique is explored in the current study. We have worked with Persian Ghazal, which Hafez writes. We have found and employed useful NLP approaches to facilitate and automate the classification of Hafez’s poetry. We developed and implemented a set of rigorous and repeatable techniques that may be extended to different types of poetries. It is a part of Persian text classification and NLP. We have implemented neural network models that automatically classify Hafez’s Persian poetry chronologically with around 85% accuracy. This proposed model is significantly better than previously reported work in the Persian Language on poetry data. In the Persian language, meter classification and machine learning-based poetry classification were done before. We have introduced a classification method based on the poet’s era using sequential architectures. We found the highest accuracy when we used the Distributed Memory model for document embedding and Long Short-Term Memory (LSTM) model for training the Persian Hafez ghazals. We have achieved approximately 87% precision, 85% F1-score and 85% recall score by using our model. To perform this classification, we have used refined Hafez ghazals’ labels and found better accuracy than Bidirectional Long Short-Term Memory (Bi-LSTM) and Gated Recurrent Units (GRU) models.