The paper titled "A Data-Driven Study of Hope in Early Arabic Poetry" explores the expression of hope in early Arabic poetry through a data-driven approach. The study analyzes a corpus of pre-Islamic Arabic poems to uncover themes related to hope, utopianism, and societal aspirations. The research employs natural language processing (NLP) tools, particularly focusing on lemmatization and part-of-speech tagging, to annotate the poems and facilitate a deeper analysis. The paper also explores the potential of using AI models like GPT-3 for sentiment analysis, despite challenges related to the model's consistency and accuracy.
The study identifies five major themes where hope is expressed: metaphysical visions, the need for peace, hopes related to travel and separation, morals and mental attitudes, and the psychology of hope. Despite the limitations of current NLP tools and the incompleteness of the available corpus, the research demonstrates that a data-driven approach can effectively highlight the presence of utopian thoughts in early Arabic poetry. The study concludes by recommending further refinement of the methods and tools used, as well as the expansion of lemma sets to improve the accuracy and scope of future research.
