Thesis Abstract

Abstract
This thesis presents the development of a machine learning algorithm for sentiment analysis in social media data. Sentiment analysis, also known as opinion mining, is a natural language processing technique used to identify and extract subjective information from text data. In the context of social media, sentiment analysis plays a crucial role in understanding public opinion, sentiment trends, and user feedback. This research project aims to address the challenges associated with sentiment analysis in social media data by designing and implementing an efficient machine learning algorithm. The first chapter of the thesis provides an introduction to the research topic, discussing the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. The second chapter consists of a comprehensive literature review that covers ten key aspects related to sentiment analysis, machine learning algorithms, social media data processing, and sentiment classification techniques. Chapter three focuses on the research methodology employed in this study. It includes detailed descriptions of the data collection process, data preprocessing techniques, feature extraction methods, model selection, model training, and evaluation metrics. Additionally, the chapter outlines the experimental setup and validation procedures used to assess the performance of the developed machine learning algorithm. Chapter four presents an elaborate discussion of the findings obtained from the experimental evaluation of the machine learning algorithm. The results are analyzed and interpreted to evaluate the effectiveness, efficiency, and accuracy of the sentiment analysis model in social media data. The chapter also discusses the implications of the findings and identifies potential areas for future research and improvement. Finally, chapter five provides a conclusion and summary of the project thesis. The key findings, contributions, limitations, and implications of the research are summarized, and recommendations for future work are proposed. Overall, this research project contributes to the field of sentiment analysis by developing a novel machine learning algorithm that enhances the accuracy and efficiency of sentiment classification in social media data. Keywords Sentiment Analysis, Machine Learning, Social Media Data, Natural Language Processing, Opinion Mining, Text Classification.

Thesis Overview