Decoding Imagist Poetry Through Artificial Intelligence: A Deep Learning Framework for Literary Analysis and Translation Studies

This research presents how artificial intelligence tools, specifically deep learning models, help examine Imagist poetry. Through the utilization of natural language processing (NLP) techniques, our analysis reveals hidden patterns alongside the unique thematic and stylistic elements of Imagist poets providing a deeper understanding of their creative process and artistic goals. The Imagist literary movement makes an ideal analytical candidate because its poems maintain precise language power through sparse but meaningful image descriptions. Analytical methods include word cloud visualizations, sentiment analysis, and feature annotations, revealing recurring imagery, emotional shifts, and progression graphs which enable sophisticated examination of thematic patterns. AI-based imagery detection tools along with rhythm analysis algorithms help translators identify essential artistic components that need preservation in the work translation. This research highlights AI’s role as both a motif detector and a bridge between traditional literary methods and modern computational technologies, offering scholars and translators innovative ways to study Imagist poetry.