Enhanced Mood and Theme Recognition in Music Using Lyrical Sentiment Analysis

Abstract

This paper presents an innovative approach to mood and theme recognition in music by leveraging lyrical sentiment analysis and audio signal processing techniques. As music plays a crucial role in emotional expression and cultural representation, understanding the mood and thematic elements within songs is essential for enhancing the listening experience. Traditional methods of music analysis often focus separately on audio signals or lyrics, failing to capture the intricate relationship between the two. To address this gap, we propose a hybrid model that integrates deep learning algorithms, such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs), with Natural Language Processing (NLP) techniques to provide a comprehensive understanding of musical content. In our study, we employ the CAL500 dataset, which contains a diverse selection of songs to facilitate effective analysis. The first component of our methodology involves lyrical sentiment analysis, where the preprocess the lyrics through techniques like tokenization, stemming, and stop-word removal to extract emotional context. This is complemented by audio signal processing, where features such as tempo, rhythm, and pitch are extracted using libraries like Librosa, allowing for a nuanced understanding of the music's tonal characteristics. The integration of insights derived from lyrical sentiment and audio signal processing enables the development of a robust classification system that accurately identifies mood and themes in music. Our results indicate that the combined approach significantly enhances mood recognition accuracy and reveals recurring themes within song lyrics, providing deeper insights into the underlying narrative and artistic intent. Ultimately, this research contributes to the fields of music theory, machine learning, and emotional analysis by proposing a novel framework for understanding the emotional landscape of music. The anticipated outcomes include improvements in music classification accuracy, insights into the emotional and cultural significance of songs, and potential applications in music therapy and personalized music experiences. By bridging characteristics, this study aims to transform how listeners interact with music, enhancing their emotional connection and appreciation of this art form