2021/04/17 by Avishek Das, Omar Sharif, Das, Avishek +5
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Sentiment Analysis and Opinion Mining #Spam and Phishing Detection #Text and Document Classification Technologies
paper · pdf · doi:10.48550/arxiv.2104.08613
openalex publication_date 2021/04/17 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
Although research on emotion classification has significantly progressed in\nhigh-resource languages, it is still infancy for resource-constrained languages\nlike Bengali. However, unavailability of necessary language processing tools\nand deficiency of benchmark corpora makes the emotion classification task in\nBengali more challenging and complicated. This work proposes a\ntransformer-based technique to classify the Bengali text into one of the six\nbasic emotions: anger, fear, disgust, sadness, joy, and surprise. A Bengali\nemotion corpus consists of 6243 texts is developed for the classification task.\nExperimentation carried out using various machine learning (LR, RF, MNB, SVM),\ndeep neural networks (CNN, BiLSTM, CNN+BiLSTM) and transformer (Bangla-BERT,\nm-BERT, XLM-R) based approaches. Experimental outcomes indicate that XLM-R\noutdoes all other techniques by achieving the highest weighted f1-score of\n69.73 % on the test data. The dataset is publicly available at\nhttps://github.com/omar-sharif03/NAACL-SRW-2021.\n