Analisis Sentimen Pinjaman Online di Twitter dengan Metode Naive Bayes Classifier dan SVM

 (*)Ray Shandy Arischo Mail (Univeritas Teknokrat Indonesia, Bandar Lampung, Indonesia)
 Damayanti Damayanti (Univeritas Teknokrat Indonesia, Bandar Lampung, Indonesia)

(*) Corresponding Author

Submitted: January 26, 2024; Published: April 30, 2024

Abstract

Online loans are a form of financial service that occurs online or online, where online loans are available in applications or information technology. Online loans can also be a place to develop small and medium enterprises, because they provide easy access to loans and are also relatively safe. The social media platform twitter is one of the platforms that discusses illegal and legal online loans. Twitter has a trending topic feature that displays topics of conversation that are being discussed at a certain time. This research uses sentiment analysis which is useful as access to track public responses to an object of interest. In this study using a comparison of algorithms, namely naïve bayes classifier with support vector machine (SVM), where from the two methods will be sought who is better at analyzing data with which value of accuracy, precision, recall, f1-score is better. The data used in as many as 2725 tweets obtained through the crawling process with the python programming language and google collaboratory tools. Sentiment analysis is divided into 3 categories, namely positive, negative, and neutral, with data calculations divided into 70% training data and 20% test data. The naïve bayes classifier algorithm has an accuracy value of 55%, with a support of 404 data. Meanwhile, the support vector machine (SVM) accuracy is 77% with a support of 818 data. The results of the accuracy value of the SVM method are better than the naïve bayes classifier method in this study.

Keywords


Naïve Bayes Classifier; Python; Pinjaman Online; SVM; Twitter

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