Classification of poetry text into the emotional state using Deep Learning

Poetry can elicit powerful emotions on the basis of elevated language and literary devices, and placing these emotions becomes problematic owing to the subjectivity of human emotions and the complexity entwined in poetic expression. This research puts forward a deep learning-based approach towards automatic classification of poetry into emotions like happiness, sadness, anger, fear, and surprise. The proposed approach also engages advanced NLP techniques that aid in discovering the semantics and emotional hinterlands of poems. For the purpose of this study, we represent poetic texts in higher-dimensional space using word embeddings such as Word2Vec and GloVe as reliable representations of contextual meanings. Recurrent Neural Networks (RNNs), particularly Long Short-Term Memory (LSTM) networks, have been employed to cater to the sequential nature of poems, thus recognizing complex patterns of emotions. Furthermore, we explore Transformer-based models such as BERT because of their superior power to understand context and nuances of emotion in text. The dataset was a curated collection of poems with annotations for emotions. Then, to ensure robust results, the performance of the models was evaluated in terms of accuracy, precision, recall, and F1-score. Initial results showed promising accuracy for such emotional identification techniques, emphasizing the aptitude of the proposed deep learning techniques to understand and elucidate poetry’s emotional content. The offered study adds a new light on the conjunction of computational linguistics and emotional analysis, bettering the understanding of automated literary analysis.