Improving Sentiment Analysis By Emotion Lexicon Approach on Vietnamese Texts

TL;DR

This paper introduces a method that combines emotion lexicons with classification models to improve sentiment analysis accuracy on Vietnamese texts, demonstrating enhanced performance through experimental validation.

Contribution

It presents a novel approach integrating emotion lexicons with classification models specifically for Vietnamese sentiment analysis.

Findings

Improved sentiment classification accuracy with the proposed method

Emotion lexicon integration enhances model performance

Experimental results confirm the effectiveness of the approach

Abstract

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.