The internet has revolutionized the way most people shop. Flexibility, convenience, products’ variations, better price, and more privacy contribute to the exponential growth of online shopping platforms. However, due to the nature of online shopping, customers are not able to physically test the product before purchasing. They rely on the information given by the seller and previous customers’ ratings to make their decision. Sometimes, the information that is given by sellers may be fraudulent, mis leading, or over claim. Many researchers had found out that ratings and other customers’ reviews can be manipulated and did not reflect on the actual customers’ sentiment on the particular product. This research investigates how sentiment analysis can be u sed as an alternative solution to measure the positive, negative, and neutral feedback of the past reviews. It is to offer more comprehensive way to help the customers make an informed decision for the product that they wish to buy based on the totality of the reviews. This paper makes a comparative study on sentiment analysis methods on online shopping reviews. This can lead to the proposed theoretical framework of an alternative solution for better insight exploration. It is envisaged that this research w ould benefit the customer in making a better decision when doing online shopping and may act as a feedback mechanism for the seller to provide good products and services. A good product rating can influence many new buyers and increase business revenue and expansion.