The study's aims to create a web tool designed to help general users navigate through the reviews on the e-commerce website and help them navigate relevant product reviews to the irrelevant ones. This tool will help in giving users information on the relevancy of the reviews that are displayed on e-commerce websites with the aim of making the shopping experience easier and more efficient. Convenient sampling was utilized and was conducted with 30 respondents gathered from different age brackets including Gen Z composed of ages 18-24 mostly teenagers, Millennials ages 25-40, Gen X, Merchants & CS/IT Professionals that were part of the respondents who answered the technical and nontechnical questionnaire based from the ISO 25010 standard, ensuring that the respondents were knowledgeable in using e-commerce platforms. Data from the e-commerce website was gathered using Selenium automation to extract data from the testing environment and processing it with Cross-correlation Template Matching algorithm for images and Multinomial Naive Bayes algorithm for text sentiment analysis, combining the results using a weighted average with weights 50/50 for both algorithms. Although the results were similar to simple mean, weighted mean was chosen for easier manipulation of values during testing. The results were determined by a 4-point likert-type rating scale with proper colors for easier understanding: 76%-100% for Highly Relevant (Darker Green), 51%-75% for Relevant (Light Green), 26%-50% for Slightly Relevant (Orange), and 0%-25% for Irrelevant (Red) results.