AI Advances in Sentiment Analysis of Persian Poetry

Recent research has leveraged large language models (LLMs), specifically BERT and GPT, to analyze the sentimental depths of Persian poetry. By focusing on the works of poets Jalal al-Din Muhammad Rumi and Parvin E'tesami, the study demonstrates these models' capability to interpret complex literary nuances. The research indicates that the GPT4o language model is particularly adept in sentiment analysis, revealing that Rumi’s verses generally convey more positive sentiments when evaluated against the metrics of E'tesami's poetry.

The implications of these findings suggest a shift in literary analysis, with AI tools able to conduct detailed textual evaluations devoid of human bias. This progress enhances our understanding of poetic sentiment and opens avenues for further exploration in cultural literature through AI. The study indicates the potential for LLMs to not only analyze but also to contribute to the field of literary studies, creating more objective assessments of literary works.