Thesis Abstract

Abstract
This research project focuses on the development of a machine learning algorithm for sentiment analysis of social media data. As social media platforms continue to grow in popularity and influence, it has become increasingly important for companies and individuals to understand the sentiments expressed by users on these platforms. Sentiment analysis, a branch of natural language processing, plays a crucial role in extracting and analyzing opinions, emotions, and attitudes expressed in text data. The main objective of this study is to design and implement a machine learning algorithm that can effectively classify social media data into positive, negative, or neutral sentiments. The algorithm will be trained on a large dataset of social media posts and comments, utilizing techniques such as text preprocessing, feature extraction, and sentiment classification. Various machine learning models, including Support Vector Machines, Naive Bayes, and Neural Networks, will be evaluated to determine the most suitable approach for sentiment analysis in this context. Chapter one provides an introduction to the research topic, background information on sentiment analysis and social media data, the problem statement, objectives of the study, limitations, scope, significance, structure of the thesis, and definitions of key terms. Chapter two consists of a comprehensive literature review covering ten key areas related to sentiment analysis, machine learning algorithms, social media data analysis, and previous studies in sentiment analysis. Chapter three outlines the research methodology, including data collection methods, data preprocessing techniques, feature extraction procedures, model selection, training, and evaluation processes. This chapter also discusses the tools and technologies used in the development of the machine learning algorithm for sentiment analysis. Chapter four presents a detailed discussion of the findings obtained from implementing and testing the machine learning algorithm on social media data. The chapter analyzes the performance of different machine learning models, evaluates the accuracy of sentiment classification, and discusses the implications of the results for sentiment analysis in social media. Finally, chapter five concludes the thesis by summarizing the key findings, discussing the contributions of the research, highlighting its limitations, and suggesting areas for future research. Overall, this study aims to provide valuable insights into sentiment analysis of social media data and contribute to the development of more accurate and efficient machine learning algorithms for sentiment analysis applications.

Thesis Overview