THE ROLE OF NATURAL LANGUAGE PROCESSING IN AUTOMATING CUSTOMER SERVICE AND ENHANCING USER EXPERIENCE IN E-COMMERCE PLATFORMS

Abstract

The rapid growth of e-commerce has intensified the need for intelligent, scalable, and customer-centric communication systems. This study explores the application of Natural Language Processing (NLP) and deep learning techniques to enhance customer service, sentiment analysis, and personalized recommendations in e-commerce environments. A mixed-methods experimental approach was employed, integrating quantitative performance evaluation with qualitative analysis of customer interactions. Large-scale textual datasets, including customer queries, reviews, and transaction histories, were processed using transformer-based NLP models to assess accuracy, precision, recall, and F1-score across multiple tasks. The results demonstrate that NLP-driven chatbots and recommendation systems achieve consistently high performance, significantly reducing response time while improving contextual relevance and customer satisfaction. Sentiment analysis models effectively captured customer emotions and opinions, enabling proactive service improvements and data-driven decision-making. Visual and statistical analyses further confirmed system scalability and robustness under increasing interaction volumes. Overall, the findings validate the effectiveness of NLP and deep learning in delivering intelligent, adaptive, and personalized e-commerce experiences. The study contributes empirical evidence supporting the integration of advanced NLP solutions to improve operational efficiency, customer engagement, and long-term business value in digital commerce platforms.