Utilizing Computational Linguistics Tools for Enhanced Poetic Interpretation

Abstract

This paper explores the functionalities of Python libraries, such as SpaCy and TextBlob, and pairs them with the ChatGPT-4 to undertake an analytical expedition into the realm of poetry. The study found that Python's tools adeptly handle tokenization, sentiment detection, and semantic analysis but are confined to analyzing specific text within the boundaries of their pre-trained models. In contrast, ChatGPT-4 merges advanced natural language processing (NLP) techniques with state-of-the-art machine learning paradigms, enabling a more comprehensive range of analyses covering thematic, imagery, and contextual dimensions. This fusion of traditional literary methodologies with advanced computational techniques illuminates the prospect of deciphering the nuanced linguistic constructs and profound thematic layers crafted by poets, thus offering layered insights into poetry. Acknowledging the restrictions posed by Python's modular libraries, our study was meticulous in the text selection. The paper analyzes an original poem by the author, performing NLP tasks such as sentiment analysis, emotion and tone detection, and dependency parsing using Python. In comparison, ChatGPT-4 showcases its prowess by not only capturing the surface-level natural imagery within the poem but also highlighting its underlying tribute to national pride and the contemplation it invites on the essence of freedom. The paper concludes by discussing the inherent limitations of Python-based NLP and ChatGPT-4 and suggests future research directions to bridge the gap between human intuition and technological innovation for deeper poetic insights.