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

2021-04-17 18:28:39
Avishek Das, Omar Sharif, Mohammed Moshiul Hoque, Iqbal H. Sarker
arXiv_CL
             
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 this https URL.

Abstract (translated)

URL

https://arxiv.org/abs/2104.08613

PDF

https://arxiv.org/pdf/2104.08613.pdf

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