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

arXiv: https://arxiv.org/abs/2104.08613 · HTML: https://mlsanity.com/paper/2104.08613/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 17 Apr 2021.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [ktrain: A Low-Code Library for Augmented Machine Learning](https://mlsanity.com/paper/2004.10703/) (2020)
- [BNLP: Natural language processing toolkit for Bengali language](https://mlsanity.com/paper/2102.00405/) (2021)
- [Transformer-based Language Model Fine-tuning Methods for COVID-19 Fake News Detection](https://mlsanity.com/paper/2101.05509/) (2021)
- [ANA at SemEval-2019 Task 3: Contextual Emotion detection in Conversations through hierarchical LSTMs and BERT](https://mlsanity.com/paper/1904.00132/) (2019)
- [Anubhuti -- An annotated dataset for emotional analysis of Bengali short stories](https://mlsanity.com/paper/2010.03065/) (2020)
- [Fine-Grained Emotion Classification of Chinese Microblogs Based on Graph Convolution Networks](https://mlsanity.com/paper/1912.02545/) (2019)

## Built on by (most cited first, 7 of 7)

- [LowResource at BLP-2023 Task 2: Leveraging BanglaBert for Low Resource Sentiment Analysis of Bangla Language](https://mlsanity.com/paper/2311.12735/) (2023)
- [Transformer-based Text Classification on Unified Bangla Multi-class Emotion Corpus](https://mlsanity.com/paper/2210.06405/) (2022)
- [MONOVAB : An Annotated Corpus for Bangla Multi-label Emotion Detection](https://mlsanity.com/paper/2309.15670/) (2023)
- [RSM-NLP at BLP-2023 Task 2: Bangla Sentiment Analysis using Weighted and Majority Voted Fine-Tuned Transformers](https://mlsanity.com/paper/2310.14261/) (2023)
- [Analysing the Greek Parliament Records with Emotion Classification](https://mlsanity.com/paper/2205.12012/) (2022)
- [BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning](https://mlsanity.com/paper/2608.05104/) (2026)
- [Two Emojis of Difference: What Multilingual Affective Generation Benchmarks Actually Measure](https://mlsanity.com/paper/2609.29445/) (2026)

## Abstract (arXiv)

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 f1-score of 69.73% on the test data. The dataset is publicly available at https://github.com/omar-sharif03/NAACL-SRW-2021.