AI-Enhanced Literary Analysis: Exploring Machine Learning Techniques for Understanding English Literary Texts

Translating and analyzing literary works in English literary texts, or the other way around, is known as cross-language research in English literature. This multifaceted task involves several elements, such as literary comprehension, cultural adaption, and linguistics conversion. People from a variety of language origins may study and enjoy the depth of English literature by using cross-language examinationto make literary texts initially published in English more widely available. This study presents a novel artificial intelligence (AI) paradigm for examining contemporary English literature poetries, which aligns with the worldwide Sustainable Growth Objectives and interdisciplinary methods fusing AI and sustainability. It goes beyond conventional subjective evaluations by using feature extraction and machine-learning methods to categorize poetry. The system's accuracy and generalization will be improved by extending its application to a wider range of literary forms and approaches. Furthermore, initiatives will concentrate on equitably implementing AI tools to provide access to high-quality learning. This study sheds light on the way AI might improve poetry instruction and support environmental objectives. It paves the way for significant advancements in electronic literature poetry and academic innovation by resolving the noted drawbacks and incorporating the approach into learning environments.