Topic Modeling and Transformer Sentiment Analysis of English National Anthems

Background: A national anthem of a nation is asymbolic mirror of its history, values, and identity. Often, thelyrics of these musical pieces express the common feelings ofpride, struggle, and hope. Though they are culturally signifi-cant, little research has carefully looked at the semantic topicsand emotional tthe research of these terms. Using topic model-ing and transformer-based sentiment analysis to national an-thems written in English from various countries, this paperseeks to close this gap. Methodologies: The method is sentimentanalysis using a fine-tuned BERT-based model, latent thematicstructure extraction with Latent Dirichlet Allocation (LDA),and preprocessing of the anthem texts. Our findings show thatthe three most prevalent themes in anthems are divine invoca-tion, resistance, and patriotism. Sentiment research indicatesthat generally there is a good emotional the research combinedwith clear expressions of will and sacrifice. Result and Discus-sion: A study of the transformer method in relation to moreconventional lexicon-based models and RNN models revealsthat the transformer method provides improved accuracy andcontextual awareness. By combining cultural texts with ma-chine learning, this work contributes to digital humanities. Itaccomplishes this by revealing subtle awareness of the nationalethos and historical narrative interwoven into anthems.