Improving Sentiment Analysis By Emotion Lexicon Approach on Vietnamese Texts
This work addresses sentiment analysis for Vietnamese language applications, but it is incremental as it applies an existing emotion lexicon approach to a specific domain.
The authors tackled sentiment analysis on Vietnamese texts by combining an emotion lexicon with classification models, resulting in improved model performance.
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.