Public sentiment identification in social media for code-mixed English-Bangla and Banglish text analysis with machine learning

Social media has become a primary source of communication, bringing together people with diverse opinions and backgrounds. Identifying these mixed emotions, especially in code-mixed English-Bangla and Banglish texts, presents unique challenges. This thesis explores machine learning techniques to accurately detect and analyze positive and negative sentiment in such multilingual environments, contributing to a safer and more constructive online community. Sentiment analysis, or opinion mining, is the study of how people’s views, sentiments, assessments, attitudes, and emotions are conveyed in writing. It is one of the most active research areas in Natural Language Processing (NLP) due to its relevance for business and society, expanding beyond computer science into management and social sciences. This study provides an overview of the challenges associated with sentiment analysis, focusing on its methods and procedures. It reviews the latest research using deep learning techniques to address sentiment analysis issues. Specifically, this research applied word embedding techniques and models to datasets. A comparative analysis of experimental results for various models and input features was conducted, highlighting the effectiveness of advanced machine learning approaches in sentiment analysis.