Transformer-based Text Classification on Unified Bangla Multi-class Emotion Corpus
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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.
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
11 of 69 references.
References · 11
- 2019SemEval-2017 Task 4: Sentiment Analysis in Twittercited by 210
- 2018EmotionLines: An Emotion Corpus of Multi-Party Conversationscited by 80
- 2017WASSA-2017 Shared Task on Emotion Intensitycited by 38
- 2019Dimensional Emotion Detection from Categorical Emotioncited by 25
- 2020Personality Trait Detection Using Bagged SVM over BERT Word Embedding Ensemblescited by 20
- 2019EmotionX-IDEA: Emotion BERT -- an Affectional Model for Conversationcited by 13
- 2019ANA at SemEval-2019 Task 3: Contextual Emotion detection in Conversations through hierarchical LSTMs and BERTcited by 12
- 2019The evolution of argumentation mining: From models to social media and emerging toolscited by 9
- 2021Emotion Classification in a Resource Constrained Language Using Transformer-based Approachcited by 7
- 2020SentiLSTM: A Deep Learning Approach for Sentiment Analysis of Restaurant Reviewscited by 4
- 2022Efficient approach of using CNN based pretrained model in Bangla handwritten digit recognitioncited by 2
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Citing papers per year: 2023: 2.
2 papers.