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.

Thesis Overview

The project titled "Analysis of Sentiment in Song Lyrics Using Natural Language Processing" aims to explore the sentiment expressed in song lyrics through the application of Natural Language Processing (NLP) techniques. Music is a powerful medium through which artists convey emotions, thoughts, and messages to their audience. By analyzing the sentiment in song lyrics, we can gain insights into the emotional content of songs, understand the themes and mood conveyed by artists, and explore the impact of these sentiments on listeners. Natural Language Processing is a branch of artificial intelligence that focuses on the interaction between computers and human language. By utilizing NLP techniques such as sentiment analysis, text mining, and machine learning algorithms, we can extract and analyze sentiment from textual data, in this case, song lyrics. This allows us to quantify and categorize the emotional content of songs, providing a systematic approach to understanding the sentiment expressed in music. The research will involve collecting a diverse dataset of song lyrics from different genres and time periods to ensure a comprehensive analysis. The lyrics will be pre-processed to remove noise, tokenize the text, and prepare it for sentiment analysis. Various NLP tools and libraries will be utilized to conduct sentiment analysis on the song lyrics, identifying positive, negative, and neutral sentiments expressed in the text. The project will also explore the challenges and limitations of sentiment analysis in song lyrics, including issues related to context, ambiguity, and cultural references. By addressing these challenges, we aim to enhance the accuracy and reliability of sentiment analysis results in the context of music lyrics. Furthermore, the research will investigate the potential applications and implications of sentiment analysis in song lyrics. This includes understanding how sentiment analysis can be used in music recommendation systems, emotional analysis of songs, and exploring the relationship between sentiment in lyrics and audience reception. Overall, the project "Analysis of Sentiment in Song Lyrics Using Natural Language Processing" seeks to contribute to the intersection of music, technology, and emotion by leveraging NLP techniques to analyze the sentiment expressed in song lyrics. Through this research, we aim to deepen our understanding of the emotional nuances in music and enhance the ways in which we interpret and appreciate songs in the digital age.