Unveiling Emotions and Themes in Thai Songs via Topic Modeling

This paper presents a comprehensive analysis of Thai song lyrics through the application of Latent Dirichlet Allocation (LDA), exploring the thematic and emotional landscapes embedded within. By leveraging advanced computational linguistics techniques, this study uncovers the dominant themes such as love, personal growth, cultural identity, and their prevalence across a diverse corpus of lyrics. The findings illustrate how music serves as a mirror to societal values and individual experiences, revealing a nuanced tapestry of life's narratives that resonate deeply within Thai culture. Employing the Gensim library for LDA model implementation, the research meticulously fine-tunes model parameters, including topic numbers and hyperparameters, to ensure optimal thematic distinction and coherence. The analysis benefits from both quantitative metrics, like Coherence and Perplexity Scores, and qualitative expert evaluations, validating the cultural relevance and authenticity of the identified themes. Key contributions of this work include the innovative use of computational techniques in cultural analytics, enhancing our understanding of thematic and emotional dimensions in music. The study not only sheds light on the intricacies of Thai song lyrics but also demonstrates the potential of computational approaches in the arts and humanities. By providing artists and the music industry with data-driven insights and enriching cultural studies, this research underscores the symbiotic relationship between music, language, and society, paving the way for future interdisciplinary explorations.