Optimizing Customer Satisfaction Through Sentiment Analysis: A BERT-Based Machine Learning Approach to Extract Insights

In the era of digital transformation, customer feedback has become crucial for improving service quality. This study aims to enhance customer satisfaction through sentiment analysis utilizing machine learning techniques, with additional case studies conducted to ensure comprehensive method validation. Traditional sentiment analysis methods frequently fail to manage the complexity and volume of feedback data, yielding to less accurate insights. To address this challenge, we analyzed six machine learning models: Naïve Bayes, Support Vector Machine (SVM), Long Short-Term Memory (LSTM), Random Forest, AdaBoost, and BERT, with a particular focus on BERT. Our results demonstrate that BERT outperformed the other models in terms of both accuracy and processing speed, achieving an accuracy of up to 95%. The excellence of BERT in managing large and complex datasets provides a more precise sentiment analysis, which can significantly improve service quality and customer loyalty, while increasing company revenue by up to 15%. This research advances to the field of sentiment analysis by validating the effectiveness of BERT over traditional models through extensive comparative analysis and practical case studies.