Developing a Deep Learning Model for Sentiment Analysis in Social Media Posts

Thesis Abstract

Abstract
This thesis presents the development and implementation of a deep learning model for sentiment analysis in social media posts. Sentiment analysis, also known as opinion mining, aims to automatically determine the sentiment expressed in text data, such as positive, negative, or neutral. With the exponential growth of social media platforms, there is a need for efficient and accurate sentiment analysis techniques to extract valuable insights from the vast amount of user-generated content. The research begins with a comprehensive introduction discussing the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definitions of key terms. The literature review in Chapter Two explores existing sentiment analysis techniques, deep learning models, and applications in social media sentiment analysis. Ten key items are discussed, highlighting the evolution of sentiment analysis and the state-of-the-art methods in the field. Chapter Three details the research methodology employed to develop the deep learning model for sentiment analysis. The methodology includes data collection, preprocessing, feature extraction, model selection, training, and evaluation. The chapter also covers aspects such as data sources, dataset selection, data preprocessing techniques, neural network architectures, hyperparameter tuning, and evaluation metrics. In Chapter Four, the findings of the deep learning model for sentiment analysis in social media posts are presented and discussed in detail. The performance of the model is evaluated based on accuracy, precision, recall, F1-score, and other relevant metrics. The discussions delve into the strengths and limitations of the model, highlighting areas for further improvement and future research directions. Finally, Chapter Five provides a comprehensive conclusion and summary of the thesis. The key findings, contributions, implications, and recommendations for future research are discussed. The thesis concludes by emphasizing the importance of sentiment analysis in social media and the potential applications of the developed deep learning model in real-world scenarios. Overall, this thesis contributes to the field of sentiment analysis by developing a deep learning model tailored for social media posts. The model demonstrates promising results in accurately classifying sentiments and extracting valuable insights from social media data. The research opens up avenues for further advancements in sentiment analysis techniques and their applications in diverse domains.