首页|Research Results from Nasional University Update Understanding of Machine Learni ng (Optimizing Customer Satisfaction Through Sentiment Analysis: A BERT-Based Ma chine Learning Approach to Extract Insights)
Research Results from Nasional University Update Understanding of Machine Learni ng (Optimizing Customer Satisfaction Through Sentiment Analysis: A BERT-Based Ma chine Learning Approach to Extract Insights)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in artific ial intelligence.According to news reporting out of Jakarta, Indonesia, by News Rx editors, research stated, “In the era of digital transformation, customer fee dback has become crucial for improving service quality.”The news reporters obtained a quote from the research from Nasional University: “This study aims to enhance customer satisfaction through sentiment analysis uti lizing machine learning techniques, with additional case studies conducted to en sure comprehensive method validation.Traditional sentiment analysis methods fre quently fail to manage the complexity and volume of feedback data, yielding to l ess accurate insights.To address this challenge, we analyzed six machine learni ng models: Naive Bayes, Support Vector Machine (SVM), Long Short-Term Memory (LS TM), Random Forest, AdaBoost, and BERT, with a particular focus on BERT.Our res ults demonstrate that BERT outperformed the other models in terms of both accura cy and processing speed, achieving an accuracy of up to 95%.The ex cellence 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%.”
Nasional UniversityJakartaIndonesiaAsiaCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
