mlsanityData to 7 Oct 2026

Improving Sentiment Analysis By Emotion Lexicon Approach on Vietnamese Texts

arXiv 2210.02063 ↗First posted 5 Oct 2022cs.CLcited by 2

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Abstract (arXiv)

The sentiment analysis task has various applications in practice. In the sentiment analysis task, words and phrases that represent positive and negative emotions are important. Finding out the words that represent the emotion from the text can improve the performance of the classification models for the sentiment analysis task. In this paper, we propose a methodology that combines the emotion lexicon with the classification model to enhance the accuracy of the models. Our experimental results show that the emotion lexicon combined with the classification model improves the performance of models.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 15 of its 25 references that are in the map. First posted on arXiv 5 Oct 2022.

Builds on

15 of 25 references.

References · 15

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3 more
  1. 2020A Simple and Efficient Ensemble Classifier Combining Multiple Neural Network Models on Social Media Datasets in Vietnamesecited by 8
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Cited by year

Citing papers per year: 2023: 1; 2024: 0; 2025: 1.

2 papers.

  1. 2023ViCGCN: Graph Convolutional Network with Contextualized Language Models for Social Media Mining in Vietnamesecited by 1
  2. 2025NEU-ESC: A Comprehensive Vietnamese dataset for Educational Sentiment analysis and topic Classification toward multitask learningcited by 0