Investigation Emotion Dynamics in Bangla Social Media Text for Improved Emotion Detection

Recognizing emotion from text is fundamental to machine learning and influences our understanding of human interaction. While English sentiment analysis has been well-researched, Bengali (Bangla) is still under-researched. Our study bridges this gap by investigating the emotional dynamics in Bengali social media posts using extensive user-generated content. We use a modified dataset defined by seven emotions: Happy, Surprise, Fearful, Angry, Neutral, Disgust, and Sad. Using machine learning techniques such as support vector classifiers, decision trees, logistic regression, and random forest, we found that the Random Forest model achieved the highest accuracy of 84%. This study advances our understanding of Bengali emotional expression and demonstrates its application in social media sentiment analysis, psychoanalysis, and communication technologies. By addressing the complexities of Bengali language and culture, our work contributes to a broader definition of emotion and lays the foundation for future research in different contexts.