Customer Service Sentiment Analysis of Internet Service Providers Using Support Vector Machine and Naïve Bayes Algorithms
Analisis Sentimen Layanan Pelanggan Provider Internet Dengan Algoritma Support Vector Machine Dan Naïve Bayes
DOI:
https://doi.org/10.21070/ups.8289Keywords:
Analisis Sentimen, Text Mining, Naïve Bayes, Support Vector Machine, X.comAbstract
The rise in customer complaints and compliments about internet services underscores the need to understand public sentiment. Without proper analysis, Biznet may overlook opportunities to enhance customer satisfaction. This study analyzes customer sentiment toward Biznet using Text Mining techniques with Naïve Bayes and Support Vector Machine (SVM) algorithms. Data was collected from X.com (Twitter) via Tweepy and processed through cleaning, normalization, tokenization, sentiment labeling with VADER, and TF-IDF for feature extraction. The results indicate that SVM with a Sigmoid kernel achieved the highest accuracy (94.29%), followed by Linear SVM (93.92%) and Bernoulli Naïve Bayes (88.21%). Based on performance metrics, SVM with Sigmoid kernel proved to be the most effective. This analysis provides valuable insights to help Biznet enhance services and align improvements with customer expectations.
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