用AI分析波斯诗歌情感，发现鲁米诗更积极且节奏更丰富。
Artificial Intelligence for Sentiment Analysis of Persian Poetry
- 用BERT和GPT4o模型分析两位波斯诗人作品。
- 鲁米诗情感更积极，且用韵律表达更多样情感。
- 证明大模型可无偏倚完成诗歌语义分析，适合文学研究者。
人工智能的最新进展催生了能够理解、分析和生成文本的大语言模型（LLMs），为文学尤其是诗歌分析提供了新机遇。本文采用多种基于BERT和GPT的预训练语言模型，分析两位著名波斯诗人——鲁米（Jalal al-Din Muhammad Rumi）与帕尔文·埃特萨米（Parvin E'tesami）的作品。研究旨在评估现代语言模型对波斯诗歌复杂性的理解能力，并探索诗歌情感与其格律之间的潜在关联。结果显示，GPT4o模型在分析波斯诗歌方面表现可靠；情感分析表明，总体上鲁米的诗歌比帕尔文·埃特萨米的诗歌表达更积极的情感。此外，对比格律使用情况发现，鲁米的诗歌在利用格律表达多样化情感方面更具优势。这些发现表明，大语言模型可有效应用于无需人类解读的计算机化语义研究，显著降低分析中的主观偏差。
原文摘要 · Abstract (English)
Recent advancements of the Artificial Intelligence (AI) have led to the development of large language models (LLMs) that are capable of understanding, analysing, and creating textual data. These language models open a significant opportunity in analyzing the literature and more specifically poetry. In the present work, we employ multiple Bidirectional encoder representations from transformers (BERT) and Generative Pre-trained Transformer (GPT) based language models to analyze the works of two prominent Persian poets: Jalal al-Din Muhammad Rumi (Rumi) and Parvin E'tesami. The main objective of this research is to investigate the capability of the modern language models in grasping complexities of the Persian poetry and explore potential correlations between the poems' sentiment and their meters. Our findings in this study indicates that GPT4o language model can reliably be used in analysis of Persian poetry. Furthermore, the results of our sentiment analysis revealed that in general, Rumi's poems express happier sentiments compared to Parvin E'tesami's poems. Furthermore, comparing the utilization of poetic meters highlighted Rumi's poems superiority in using meters to express a wider variety of sentiments. These findings are significant as they confirm that LLMs can be effectively applied in conducting computer-based semantic studies, where human interpretations are not required, and thereby significantly reducing potential biases in the analysis.
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