<![CDATA[The development of artificial intelligence technology has driven significant transformations in various sectors, including customer service. One of its increasingly developed applications is the use of chatbots based on Natural Language Processing (NLP). This research aims to explore the implementation of NLP in chatbots to enhance efficiency, accuracy, and customer satisfaction in digital customer service systems. By using descriptive analysis methods and case studies on several customer service platforms, this research examines how NLP components such as natural language processing, sentiment analysis, and context understanding are used to automatically and relevantly respond to customer inquiries. The analysis results show that chatbots equipped with NLP are capable of understanding human language more naturally, answering questions with appropriate context, and significantly reducing the workload of human agents. Additionally, the integration of NLP allows for personalized responses and continuous learning from previous interactions. However, there are also challenges such as limitations in understanding language ambiguity and the need for large training data. This research concludes that the implementation of NLP in chatbots is a strategic step to improve customer service quality, but it must be supported by the design of adaptive and user experience-oriented systems.]]>
