Analysis of Sentiment in Song Lyrics Using Natural Language Processing

Thesis Abstract

Abstract
This thesis presents a comprehensive study on the analysis of sentiment in song lyrics using natural language processing (NLP) techniques. The research focuses on applying advanced NLP algorithms to extract sentiment and emotions embedded within song lyrics. The study aims to investigate how sentiment analysis can be effectively utilized to gain insights into the emotional content of songs and understand the underlying sentiments conveyed by songwriters. The thesis begins with an introduction that provides an overview of the research topic and outlines the objectives and significance of the study. The background of the study delves into the current state of sentiment analysis in the context of music and highlights the importance of understanding emotional content in song lyrics. The problem statement identifies the gap in existing research and motivates the need for a more in-depth analysis of sentiment in song lyrics. The objectives of the study are to develop a sentiment analysis framework tailored for song lyrics and to explore the potential applications of sentiment analysis in the music industry. The limitations and scope of the study are also discussed to provide a clear understanding of the research boundaries and focus areas. The significance of the study lies in its potential to enhance music recommendation systems, emotional analysis of music, and understanding audience preferences. The literature review chapter presents a comprehensive analysis of existing research on sentiment analysis, natural language processing, and music analysis. The review covers various sentiment analysis techniques, emotional modeling in text, and the application of NLP in music-related studies. The research methodology chapter outlines the methodology employed in the study, including data collection, preprocessing, sentiment analysis algorithms, and evaluation metrics. The findings chapter presents the results of sentiment analysis conducted on a dataset of song lyrics. The analysis reveals patterns of sentiment distribution, emotional themes, and sentiment polarity in the lyrics. The discussion chapter interprets the findings, discusses the implications of the results, and explores potential future research directions in sentiment analysis of song lyrics. In conclusion, this thesis contributes to the field of sentiment analysis by demonstrating the feasibility and effectiveness of applying NLP techniques to analyze sentiment in song lyrics. The study provides valuable insights into the emotional content of songs and offers opportunities for enhancing music recommendation systems and understanding audience preferences based on sentiment analysis.