Customer sentiment analysis provides valuable insights for ecommerce businesses, but traditional methods often fall short in handling complex and contextrich language. This paper explores the use of Natural Language Processing (NLP) techniques, including BERT and transformer models, to improve sentiment analysis accuracy in ecommerce. The study compares the performance of different NLP models in capturing nuanced customer sentiment from online reviews. Findings indicate that advanced NLP techniques substantially increase accuracy and offer practical applications for improving customer experience and business strategy in ecommerce.Keywords: Natural Language Processing, sentiment analysis, ecommerce, customer experience, BERT, transformer models.

This article investigates disparities in access to mental health services between urban and rural communities in Indonesia. By analyzing data on service availability, utilization, and barriers to access, the study highlights factors that contribute to unequal mental health support. Findings reveal that rural populations face greater challenges in accessing mental health care, primarily due to a lack of facilities, shortage of mental health professionals, and limited awareness. The results underscore the need for policies that address these geographic health disparities and promote equitable access to mental health services.