The opinions, expectations and preferences of the customers can be extracted from the reviews posted for the products purchased in the e-commerce portal. Sentiment analysis is a powerful tool used by companies to understand the consumer behaviour. Customer sentiments play a vital role in shaping purchasing decisions and understanding their behaviour. This study explored sentiment distribution across different product categories such as electronics, clothing, and books sold from a popular e-commerce website. It used 701,238 reviews collected from a popular e-commerce website across different product categories spanning from 2016 to 2023. Understanding sentiment trends can provide valuable insights for businesses looking to optimize their offerings and marketing strategies. It used machine learning techniques to classify and analyse the sentiments expressed through text in the e-commerce portals. This study was aimed to analyze customer sentiment in three key product categories—electronics, clothing, and books and explored its relationship using sentiment distribution. Using Natural Language Processing (NLP) techniques such as sentiment analysis, topic modelling, and word cloud generation customer reviews were analysed from the e-commerce platform. This study helped in identifying key themes discussed in the reviews using unsupervised machine learning technique. This study explored sentiment distribution across different product categories sold from a popular e-commerce website. Findings of the study revealed key themes discussed in the reviews and similar sentiment distribution across different product categories. Future research directions include finding relationship between sentiment and its product ratings. It would be helpful to find the sentiment trend across product categories for deeper sentiment-based segmentation.