{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/emotion-classification-in-a-resource","title":"Emotion Classification in a Resource Constrained Language Using Transformer-based Approach","arxiv_id":"2104.08613","date":"2021-04-17","proceeding":"NAACL 2021 4","authors":["Avishek Das","Omar Sharif","Mohammed Moshiul Hoque","Iqbal H. Sarker"],"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.","url_abs":"https://arxiv.org/abs/2104.08613v1","url_pdf":"https://arxiv.org/pdf/2104.08613v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"emotion-classification-in-a-resource","repo_url":"https://github.com/sagorbrur/bangla-bert","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"emotion-classification-in-a-resource","repo_url":"https://github.com/omar-sharif03/NAACL-SRW-2021","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"emotion-classification-in-a-resource","repo_url":"https://github.com/avishek-018/Emotion-Classification-using-Transformers","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"emotion-classification-in-a-resource","repo_url":"https://github.com/avishek-018/TransEmoC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"emotion-classification","task_name":"Emotion Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"xlm-r","task_name":"XLM-R"}],"methods":[{"method_slug":"bilstm","method_name":"BiLSTM"},{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"},{"method_slug":"xlm-r","method_name":"XLM-R"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2104.08613","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}