| Summary: | The classification of emotional states from poetry or formal text has received less attention by the experts of computational intelligence in recent times as compared to informal textual content like SMS, email, chat, and online user reviews. In this study, an emotional state classification system for poetry text is proposed using the latest and cutting edge technology of Artificial Intelligence, called Deep Learning. For this purpose, an attention-based C-BiLSTM model is implemented on the poetry corpus. The proposed approach classifies the text of poetry into different emotional states, like love, joy, hope, sadness, anger, etc. Different experiments are conducted to evaluate the efficiency of the proposed system as compared to other state-of-art methods as well as machine learning and deep learning methods. Experimental results depict that the proposed model outperformed the baselines studies with 88% accuracy. Furthermore, the analysis of the statistical experiment also validates the performance of the proposed approach.
|
|---|
