CLASSIFICATION OF POETRY TEXT INTO THE EMOTIONAL STATE USING DEEP LEARNING

ABSTRACTPoetry can elicit powerful emotions on the basis of elevated language and literary devices, and placingthese emotions becomes problematic owing to the subjectivity of human emotions and the complexityentwined in poetic expression. This research puts forward a deep learning-based approach towardsautomatic classification of poetry into emotions like happiness, sadness, anger, fear, and surprise. Theproposed approach also engages advanced NLP techniques that aid in discovering the semantics andemotional hinterlands of poems. For the purpose of this study, we represent poetic texts in higherdimensional space using word embeddings such as Word2Vec and GloVe as reliable representationsof 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 recognizingcomplex patterns of emotions. Furthermore, we explore Transformer-based models such as BERTbecause of their superior power to understand context and nuances of emotion in text. The dataset wasa curated collection of poems with annotations for emotions. Then, to ensure robust results, theperformance of the models was evaluated in terms of accuracy, precision, recall, and F1-score. Initialresults showed promising accuracy for such emotional identification techniques, emphasizing theaptitude of the proposed deep learning techniques to understand and elucidate poetry's emotionalcontent. The offered study adds a new light on the conjunction of computational linguistics andemotional analysis, bettering the understanding of automated literary analysis. Keywords: Poetry, Emotion Analytics, Sentiment Analytics, Creative Writing, Listening AI, NaturalLanguage Processing, Textual Analysis, Expressive AI, Mood Poetry, Emotional Intelligence,Machine Training.