Classification of Poetry Text into the Emotional States using Deep Learning Technique

Project Code :TCMAPY1097

Objective

The objective of this project is to employ deep learning techniques for the classification of poetry text into distinct emotional states. By leveraging advanced neural network models, the aim is to develop an efficient and accurate system capable of automatically categorizing poetic expressions into predefined emotional categories. This innovative approach seeks to enhance our understanding of the emotional nuances within poetry, providing valuable insights into the intricate interplay of language and sentiment in literary works.

Abstract

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 text is proposed using the latest and innovative technology of Artificial Intelligence, called Deep Learning. For this purpose, an attention-based Bi-LSTM model is implemented on the text corpus. The proposed approach classifies the text into different emotional states, like neutral, joy, fear, sadness, anger, etc.

Keywords: Deep learning, emotion recognition, text, attention-based LSTM, formal text, emotional states

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.