Building an Affective Database for Emotion Detection from Natural Bangla Text

Emotion detection is a task within the field of Affective Computing, which focuses on designing systems and technologies capable of recognizing, interpreting, processing, and simulating human emotions. Building an affective database for Bangla text can have numerous benefits, including improving the accuracy of emotion detection, enhancing natural language processing, and providing a deeper comprehension of how people express emotions in Bangla culture. Identifying emotions from textual data and creating a consistent, reliable, and unbiased emotion dataset pose significant difficulties, especially in low-resourced languages like Bangla. This study addresses the problem of emotion detection in Bangla texts. An emotion dataset was meticulously annotated based on Paul Ekman’s six basic emotions, employing techniques to ensure dataset consistency, reliability, and impartiality. The study achieved an average Cohen’s Kappa score of 87%, indicating nearly perfect agreement between the annotator and the participants. Additionally, various machine learning models and word embeddings were evaluated for emotion detection. The findings demonstrate that Logistic Regression, SVM, and Naïve Bayes yielded the best performance, with Logistic Regression (BOW + Unigram) achieving an accuracy of 68.8% and a macro F1-score of 65%.