Analsis Sentimen Churn Pelanggan dalam Layanan Streaming NETFLIX di X Menggunakan Metode IndoBERT

Abstract

This study aims to analyze customer sentiment toward Netflix’s streaming service as expressed on social media platform X (formerly Twitter), in order to identify potential churn. The research employs a combination of Text Mining and Sentiment Analysis methods, utilizing the IndoBERT-based Natural Language Processing (NLP) model. Data was collected using web scraping techniques with keywords indicating complaints or cancellation of Netflix subscriptions. The text data underwent preprocessing steps including case folding, cleaning, lemmatization, and tokenization. Sentiment classification results showed that most tweets expressed negative sentiment, suggesting a high risk of customer churn. Key factors driving negative sentiment include subscription pricing, login policy restrictions, and the cancellation of popular content. These findings can assist Netflix’s marketing and product development teams in creating data-driven retention strategies. Furthermore, the study demonstrates that the IndoBERT model is effective in classifying Indonesian-language social media opinions into positive, neutral, and negative sentiment categories.