Emotion Classification in a Resource Constrained Language Using Transformer-based Approach

• URL: http://arxiv.org/abs/2104.08613v1
• Date: Sat, 17 Apr 2021 18:28:39 GMT
• Title: Emotion Classification in a Resource Constrained Language Using Transformer-based Approach
• Abstract summary: This work proposes a transformer-based technique to classify the Bengali text into one of the six basic emotions. A Bengali emotion corpus consists of 6243 texts is developed for the classification task.
• Score: 2.8101673772585736
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Although research on emotion classification has significantly progressed in high-resource languages, it is still infancy for resource-constrained languages like Bengali. However, unavailability of necessary language processing tools and deficiency of benchmark corpora makes the emotion classification task in Bengali more challenging and complicated. This work proposes a transformer-based technique to classify the Bengali text into one of the six basic emotions: anger, fear, disgust, sadness, joy, and surprise. A Bengali emotion corpus consists of 6243 texts is developed for the classification task. Experimentation carried out using various machine learning (LR, RF, MNB, SVM), deep neural networks (CNN, BiLSTM, CNN+BiLSTM) and transformer (Bangla-BERT, m-BERT, XLM-R) based approaches. Experimental outcomes indicate that XLM-R outdoes all other techniques by achieving the highest weighted $f_1$-score of $69.73\%$ on the test data. The dataset is publicly available at https://github.com/omar-sharif03/NAACL-SRW-2021.

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