Emotion classification of Thai text based using term weighting and machine learning techniques

N. Chirawichitchai
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引用次数: 21

Abstract

In this research, I proposed Emotion Classification of Thai Text based Using Term weighting and Machine Learning Techniques focusing on the comparison of various common term weighting schemes. I found Boolean weighting with Support Vector Machine is most effective in our experiments. I also discovered that the Boolean weighting is suitable for combination with the Information gain feature selection method. The Boolean weighting with Support Vector Machine algorithm yielded the best performance with the accuracy over all algorithms. Based on our experiments, the Support Vector Machine algorithm with the Information gain feature selection yielded the best performance with the accuracy of 77.86%. Our experimental results also reveal that feature weighting methods have a positive effect on the Thai Emotion Classification Framework.
基于术语加权和机器学习技术的泰语文本情感分类
在这项研究中,我提出了基于术语加权和机器学习技术的泰语文本情感分类,重点是比较各种常见的术语加权方案。我发现支持向量机布尔加权在我们的实验中是最有效的。我还发现布尔加权适合与信息增益特征选择方法相结合。基于支持向量机的布尔加权算法在所有算法中获得了最好的性能和精度。基于我们的实验,带有信息增益特征选择的支持向量机算法获得了最好的性能,准确率为77.86%。我们的实验结果还表明,特征加权方法对泰国情绪分类框架有积极的影响。
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