Implementation of Naïve Bayes and Decision Tree Algorithms for Sentiment Analysis on the Impact of Hydrometeorological Disasters
Implementasi Algoritma Naïve Bayes dan Decision Tree pada Sentimen Dampak Bencana Hidrometeorologi
DOI:
https://doi.org/10.21070/ups.11584Keywords:
Analysis Sentiment, Hydrometeorology Disaster, Naïve Bayes, Term Frequenncy-Invers Document Frequency (TF-IDF), Decision Tree, Social MediaAbstract
Hydrometeorological disasters often trigger public responses on social media. it’s required analysis public opinion toward such disaster. This study compares the performance of Decision Tree and Naïve Bayes Algorithm to analyze performance on analyzing sentiments of respond related to hydrometeorological disaster. The dataset that used contains of 11890 comments. Comments labeled into Positive, Negative, and neutral then processed by preprocessing, TF-IDF weighting, and classification with 5-fold cross validation. Validation results Naïve Bayes had mean accuracy by 0.79 which higher than Decision Tree, which scored 0,65. Naïve Bayes also got 0.79 score on accuracy, precision, recall, and F1-Score compares to Decision tree that scored scored 0.63, 0.70, 0.63, and 0.64 respectively. Because the gap of the mean accuracy and test accuracy wasn’t far off, concludes that Naïve Bayes have better generalization than Decision Tree. This result shows that Naïve Bayes is better than Decision Tree on classification of Hydrometeorological Disaster respond.
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