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Evaluation of classification methods for Indonesian text emotion detection

机译：印度尼西亚文本情感检测分类方法的评价

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This paper presents Indonesian text emotion detection and evaluates the performances of four different classification methods: Naive Bayes (NB), J48, K-Nearest Neighbor (KNN) and Support Vector Machine-Sequential Minimal Optimization (SVM-SMO). The experiment uses Indonesian text corpus, containing 1000 sentences which consists of six emotion classes: anger, disgust, fear, joy, sadness, and surprise. Preprocessing step which consists of tokenization, case normalization, stopword removal, stemming and TFIDF are used to extract the features of text emotion. We conduct 10-fold cross validation and split validation for the experiment. Based on the result, we conclude that SVM-SMO classifier gives the best performance. In the 10-fold cross validation, the result shows that the accuracy of NB, J48, KNN and SVM-SMO are 80.2%, 80.8%, 68.1%, and 85.5% respectively. The same conclusion is also demonstrated by the split validation, the highest accuracy of 86% is also achieved by SVM-SMO.
机译：本文提出了印度尼西亚文本情感检测，评估了四种不同分类方法的性能：天真贝叶斯（NB），J48，K最近邻（KNN）和支持向量机序列最小优化（SVM-SMO）。实验使用印度尼西亚文本语料库，其中包含1000个句子，包括六个情感课程：愤怒，厌恶，恐惧，喜悦，悲伤和惊喜。预处理步骤由令牌化，案例标准化，删除，茎和TFIDF组成，用于提取文本情绪的特征。我们进行10倍的交叉验证和分割验证进行实验。根据结果​​，我们得出结论，SVM-SMO分类器提供了最佳性能。在10倍的交叉验证中，结果表明，Nb，J48，Knn和Svm-Smo的准确性分别为80.2％，80.8％，68.1％和85.5％。通过分裂验证还证明了相同的结论，SVM-SMO也实现了86％的最高精度。

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