# Transformer-based Text Classification on Unified Bangla Multi-class Emotion Corpus

arXiv: https://arxiv.org/abs/2210.06405 · HTML: https://mlsanity.com/paper/2210.06405/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 69 references that are in the map.
- First posted on arXiv 12 Oct 2022.

## Builds on (most cited first, 11 of 11)

- [SemEval-2017 Task 4: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.00741/) (2019)
- [EmotionLines: An Emotion Corpus of Multi-Party Conversations](https://mlsanity.com/paper/1802.08379/) (2018)
- [WASSA-2017 Shared Task on Emotion Intensity](https://mlsanity.com/paper/1708.03700/) (2017)
- [Dimensional Emotion Detection from Categorical Emotion](https://mlsanity.com/paper/1911.02499/) (2019)
- [Personality Trait Detection Using Bagged SVM over BERT Word Embedding Ensembles](https://mlsanity.com/paper/2010.01309/) (2020)
- [EmotionX-IDEA: Emotion BERT -- an Affectional Model for Conversation](https://mlsanity.com/paper/1908.06264/) (2019)
- [ANA at SemEval-2019 Task 3: Contextual Emotion detection in Conversations through hierarchical LSTMs and BERT](https://mlsanity.com/paper/1904.00132/) (2019)
- [The evolution of argumentation mining: From models to social media and emerging tools](https://mlsanity.com/paper/1907.02258/) (2019)
- [Emotion Classification in a Resource Constrained Language Using Transformer-based Approach](https://mlsanity.com/paper/2104.08613/) (2021)
- [SentiLSTM: A Deep Learning Approach for Sentiment Analysis of Restaurant Reviews](https://mlsanity.com/paper/2011.09684/) (2020)
- [Efficient approach of using CNN based pretrained model in Bangla handwritten digit recognition](https://mlsanity.com/paper/2209.13005/) (2022)

## Built on by (most cited first, 2 of 2)

- [JutePestDetect: An Intelligent Approach for Jute Pest Identification Using Fine-Tuned Transfer Learning](https://mlsanity.com/paper/2308.05179/) (2023)
- [Research on the Laws of Multimodal Perception and Cognition from a Cross-cultural Perspective -- Taking Overseas Chinese Gardens as an Example](https://mlsanity.com/paper/2312.17642/) (2023)

## Abstract (arXiv)

In this research, we propose a complete set of approaches for identifying and extracting emotions from Bangla texts. We provide a Bangla emotion classifier for six classes: anger, disgust, fear, joy, sadness, and surprise, from Bangla words using transformer-based models, which exhibit phenomenal results in recent days, especially for high-resource languages. The Unified Bangla Multi-class Emotion Corpus (UBMEC) is used to assess the performance of our models. UBMEC is created by combining two previously released manually labeled datasets of Bangla comments on six emotion classes with fresh manually labeled Bangla comments created by us. The corpus dataset and code we used in this work are publicly available.