Thesis Abstract

Abstract
This thesis presents the development of a novel machine learning algorithm for sentiment analysis in social media data. Sentiment analysis, also known as opinion mining, is a computational technique used to determine the sentiment expressed in text data, such as positive, negative, or neutral. With the increasing amount of user-generated content on social media platforms, sentiment analysis has become a crucial task for understanding public opinion and sentiment towards various topics, products, and services. Traditional sentiment analysis approaches often struggle to effectively capture the nuances and complexities of social media data due to its unstructured and informal nature. Therefore, there is a need for more advanced machine learning algorithms that can accurately analyze sentiment in social media data. Chapter 1 provides an introduction to the research topic, discussing the background of sentiment analysis, the problem statement, objectives of the study, limitations, scope, significance, structure of the thesis, and key definitions of terms. Chapter 2 presents a comprehensive literature review covering ten key aspects related to sentiment analysis, machine learning algorithms, social media data analysis, and existing sentiment analysis techniques. In Chapter 3, the research methodology is detailed, outlining the data collection process, preprocessing steps, feature selection techniques, model development, training, and evaluation procedures. The chapter also discusses the selection of appropriate evaluation metrics and validation techniques for assessing the performance of the developed sentiment analysis algorithm. Chapter 4 delves into the discussion of findings, presenting the results of the experiments conducted to evaluate the proposed machine learning algorithm. The chapter analyzes the performance of the algorithm in terms of accuracy, precision, recall, and F1-score, comparing it with existing sentiment analysis approaches. The findings highlight the effectiveness and efficiency of the developed algorithm in accurately analyzing sentiment in social media data. Finally, Chapter 5 summarizes the key findings of the study, discusses the implications of the research, and provides recommendations for future work in the field of sentiment analysis. The conclusion emphasizes the significance of the developed machine learning algorithm for sentiment analysis in social media data and its potential applications in various domains, such as marketing, public opinion analysis, and social media monitoring. In conclusion, this thesis contributes to the advancement of sentiment analysis research by proposing a novel machine learning algorithm that demonstrates superior performance in analyzing sentiment in social media data. The findings of this study have important implications for understanding and interpreting public sentiment on social media platforms, offering valuable insights for decision-making and strategic planning in a data-driven world.

Thesis Overview