Key Points:

• • Current sentiment analysis tools struggle with contextual understanding and language specificity in social media data
• • A comprehensive preprocessing pipeline including emoji translation and advanced text normalization significantly improves classification accuracy
• • Five different supervised learning algorithms achieve 83-88% accuracy in classifying opinions into five distinct categories
• • The framework can be applied to analyze customer feedback across social media platforms to derive actionable business insights