# Exploiting Vietnamese Social Media Characteristics for Textual Emotion Recognition in Vietnamese

arXiv: https://arxiv.org/abs/2009.11005 · HTML: https://mlsanity.com/paper/2009.11005/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2020.

## Built on by (most cited first, 6 of 6)

- [ViSoBERT: A Pre-Trained Language Model for Vietnamese Social Media Text Processing](https://mlsanity.com/paper/2310.11166/) (2023)
- [Vietnamese Hate and Offensive Detection using PhoBERT-CNN and Social Media Streaming Data](https://mlsanity.com/paper/2206.00524/) (2022)
- [SMTCE: A Social Media Text Classification Evaluation Benchmark and BERTology Models for Vietnamese](https://mlsanity.com/paper/2209.10482/) (2022)
- [Improving Sentiment Analysis By Emotion Lexicon Approach on Vietnamese Texts](https://mlsanity.com/paper/2210.02063/) (2022)
- [ViCGCN: Graph Convolutional Network with Contextualized Language Models for Social Media Mining in Vietnamese](https://mlsanity.com/paper/2309.02902/) (2023)
- [Automatic Textual Normalization for Hate Speech Detection](https://mlsanity.com/paper/2311.06851/) (2023)

## Abstract (arXiv)

Textual emotion recognition has been a promising research topic in recent years. Many researchers aim to build more accurate and robust emotion detection systems. In this paper, we conduct several experiments to indicate how data pre-processing affects a machine learning method on textual emotion recognition. These experiments are performed on the Vietnamese Social Media Emotion Corpus (UIT-VSMEC) as the benchmark dataset. We explore Vietnamese social media characteristics to propose different pre-processing techniques, and key-clause extraction with emotional context to improve the machine performance on UIT-VSMEC. Our experimental evaluation shows that with appropriate pre-processing techniques based on Vietnamese social media characteristics, Multinomial Logistic Regression (MLR) achieves the best F1-score of 64.40%, a significant improvement of 4.66% over the CNN model built by the authors of UIT-VSMEC (59.74%).