Improving Sentiment Analysis By Emotion Lexicon Approach on Vietnamese Texts

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...

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Bibliographic Details
Published in2022 International Conference on Asian Language Processing (IALP) pp. 39 - 44
Main Authors Doan, An Long, Luu, Son T.
Format Conference Proceeding
LanguageEnglish
Published IEEE 27.10.2022
Subjects
Analytical models
Boosting
deep learning
emotion lexicons
machine learning
Semantics
Sentiment analysis
Social networking (online)
Task analysis
text classification
transformers models
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DOI10.1109/IALP57159.2022.9961318

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Summary: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.
DOI:10.1109/IALP57159.2022.9961318