Feature Selection Method for Improving Customer Reviews Classification
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Abstract
This research proposes improvement of search for feature selection techniques to increase the efficiency of customer feedback classification about restaurants, Information is collected from the website wongnai.com, a total of 4,487 messages. The research team adopted 3 techniques for selecting text features: Chi-Square, Information Gain and Information Gain Ratio to measure the effectiveness of feature selection techniques and applied Naive Bayes, Support Vector Machine, K-Nearest Neighbor and C4.5 for classification. Moreover, the 10-fold Cross Validation has been used to divide the data into a learning set and measure accuracy (Accuracy), precision (Precision) and recall (Recall). From the experiment found that Information Gain feature selection technique cooperate with the Naive Bayes technique provides the best results in the classification of comments by the accuracy is 89.08 %, the precision is 89.12 % and the recall is 89.10 %.
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