Classification of Customer Complaints on Instagram Comments Using Naïve Bayes Algorithm With N-Gram Feature Extension

Abstract

Customer complaints about the company can be used as a form of self-evaluation and performance that has been carried out by the company, based on customer complaints the company can find out the weaknesses that exist in the company and fix them. The forms of submitting customer complaints are very diverse, currently not only by telephone, but customers also submit suggestions or complaints, customers can submit suggestions or complaints via electronic mail or e-mail or forums in cyberspace that are indeed created by product-producing companies to accommodate various complaints, suggestions, and direct criticism from consumers, especially social media that are free to express opinions on the delivery services used. Instagram is a social media that is more inclined towards images and on the other hand, has captions and comments text, a study is needed for the problem of customer complaints from shipping service users on an Instagram account of a delivery service company. Based on this background, a solution is needed in solving problems for text mining classification using Naïve Bayes with SMOTE techniques and N-Gram feature extraction with the usual process for text mining so that it can produce Naïve Bayes and SMOTE accuracy with an accuracy of 88.54%, before implementation. N-Gram and the accuracy rate increased by 1.44% after the N-Gram Term was applied to 89.98% by using a dataset of 776 Instagram comment text records that had to preprocess text.

Comparison of Classification Algorithms for Analysis Sentiment of Formula E Implementation in Indonesia

“…The SMOTE (Synthetic Minority Oversampling Technique) technique treats class imbalances (Amsury, Ruhyana, Saputra, & Sulistyowati, 2020) Where is δ a random number between [0, 1]. The xnew class label is set to +1 (ynew), the minority class label.…”