Thesis Abstract

Abstract
The rapid growth of social media platforms has led to an explosion of user-generated content, making sentiment analysis a crucial task for understanding public opinion and sentiment trends. This thesis focuses on the development of a machine learning algorithm for sentiment analysis in social media data. The aim of this research is to enhance the accuracy and efficiency of sentiment analysis by leveraging the capabilities of machine learning algorithms. Chapter one provides an introduction to the research topic, presenting the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of terms. The literature review in chapter two examines existing studies on sentiment analysis, machine learning algorithms, and social media data analysis. It discusses various approaches, techniques, and tools employed in sentiment analysis research. Chapter three outlines the research methodology, detailing the data collection process, preprocessing steps, feature selection methods, and the implementation of machine learning models for sentiment analysis. It also describes the evaluation metrics used to assess the performance of the developed algorithm. The results and discussions in chapter four present the findings of the study, including the accuracy, precision, recall, and F1-score of the machine learning algorithm on social media data. The conclusion and summary in chapter five offer a comprehensive overview of the research outcomes, highlighting the contributions, limitations, and future research directions. The developed machine learning algorithm demonstrates promising results in sentiment analysis tasks, showcasing its potential for real-world applications in social media monitoring and analysis. This research contributes to the advancement of sentiment analysis techniques and expands the knowledge base in the field of machine learning and social media analytics. In conclusion, the development of a machine learning algorithm for sentiment analysis in social media data represents a significant step towards enhancing the understanding of user sentiments and opinions on online platforms. The findings of this research provide valuable insights for businesses, researchers, and policymakers seeking to leverage sentiment analysis for decision-making and trend analysis in the digital age.

Thesis Overview