Sentiment Analysis of Song Lyrics Using Machine Learning Techniques

Thesis Abstract

Abstract
Sentiment Analysis of Song Lyrics Using Machine Learning Techniques This thesis explores the application of machine learning techniques in analyzing sentiment in song lyrics. The project aims to develop a system that can automatically assess the emotional tone and sentiment expressed in song lyrics, providing valuable insights for various applications in the music industry, social media analysis, and emotional content understanding. The research begins with a comprehensive introduction, providing background information on sentiment analysis, machine learning, and the significance of analyzing sentiment in song lyrics. The problem statement highlights the challenges of manually analyzing large volumes of song lyrics and the need for automated tools to extract sentiment effectively. The objectives of the study include developing a sentiment analysis model tailored for song lyrics, evaluating its performance, and exploring potential applications in music recommendation systems and emotional content analysis. The limitations of the study are discussed, focusing on the challenges of accurately interpreting sentiment in creative and metaphorical language often found in song lyrics. The scope of the research covers the development and evaluation of the sentiment analysis model using a dataset of diverse song lyrics from various genres and time periods. The significance of the study lies in its potential to enhance music recommendation systems, improve sentiment analysis tools, and deepen our understanding of the emotional content conveyed through music. The structure of the thesis is outlined, detailing the organization of chapters and sub-sections, including the literature review, research methodology, discussion of findings, and conclusion. Definitions of key terms related to sentiment analysis, machine learning, and song lyrics are provided to ensure clarity and understanding throughout the document. The literature review chapter examines existing research on sentiment analysis, machine learning applications in music, and methodologies for analyzing emotional content in text data. Key themes such as feature extraction, sentiment classification techniques, and sentiment lexicons are explored to provide a comprehensive background for the research. The research methodology chapter outlines the data collection process, feature engineering techniques, machine learning algorithms, evaluation metrics, and experimental setup used to develop and evaluate the sentiment analysis model. The chapter also discusses the preprocessing steps for cleaning and tokenizing song lyrics, feature selection methods, and model training procedures. The discussion of findings chapter presents the results of the sentiment analysis model evaluation, including accuracy, precision, recall, and F1-score metrics. The chapter also analyzes the performance of the model across different genres, sentiment categories, and lyric lengths, highlighting strengths, limitations, and areas for further improvement. In conclusion, this thesis summarizes the key findings, contributions, and implications of the research on sentiment analysis of song lyrics using machine learning techniques. The study demonstrates the feasibility of automating sentiment analysis in song lyrics and provides insights for future research directions in music sentiment analysis and emotional content understanding.