Browsing by Author "Agostinho, Mariana Oliveira"
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- Criational: Generation of Song Lyrics with Emotional Context Using Deep Learning ModelsPublication . Agostinho, Mariana Oliveira; Malheiro, Ricardo Manuel da SilvaLanguage and music are fundamental human tools for expression, communication, and emotional connection. Music plays a central role in shaping identity, conveying feelings, and promoting social bonds, making it an intriguing domain for technological exploration. Replicating human creativity in music, especially in songwriting, presents a complex challenge, as natural language processing (NLP) and deep learning (DL) must capture both linguistic structure and emotional nuance. This study investigates the generation of emotionally contextualised song lyrics using DL models, including LSTM, GPT-2, and T5, guided by Russell’s Circumplex Model of Emotions. The models were evaluated on readability, coherence, perplexity, structural consistency, thematic alignment, and emotional accuracy. Results show that GPT-2, particularly when fine-tuned, achieves the best balance of coherence and emotional alignment, although it still lacks some musical features such as rhyme and rhythm. LSTM exhibits patterned sequences but high variability, while T5 struggles with structural consistency and repetitive output, highlighting the challenges of small, non-specialised datasets. Overall, the work confirms the feasibility of using DL models as creative support in lyric composition, capable of offering emotionally expressive material to inspire musicians, while also pointing to the need for larger datasets and models tailored to musical structure to achieve fully convincing results.