Sentiment Analysis of Ghanaian Social Media Discussion on Government Policy

The exponential growth of social media platforms has fundamentally transformed the landscape of public discourse and political communication in Ghana, creating unprecedented opportunities for citizens to engage with government policies and express their opinions on national issues. This comprehensive study investigates the application of advanced sentiment analysis techniques to understand and categorize public perceptions of government policies as expressed through social media discussions across multiple platforms, including Twitter (X), Facebook, and YouTube. The research employs a sophisticated mixed-methods approach, combining quantitative computational analysis with qualitative interpretative methods to provide a holistic understanding of public sentiment patterns. Utilizing state-of-the-art Natural Language Processing (NLP) technologies including VADER (Valence Aware Dictionary and Entiment Reasoner), TextBlob, and BERT (Bidirectional Encoder Representations from Transformers), the study systematically categorizes social media content into positive, negative, and neutral sentiment classifications with high accuracy and reliability.