This project aimed to develop an AI-based model for two taxi apps to provide a comprehensive solution for service providers to efficiently analyze large volumes of customer feedback, using natural language processing NLP-based sentiment analysis models using Python, and visualization using Power BI. An Engineering goal was set at 98% accuracy. The method involved using Google API to extract review data, followed by data cleaning and text pre-processing using Python. The model categorized reviews into positive, negative, and neutral sentiments, and additional analysis identified the top 20 words in each sentiment category. Power BI was used to create interactive dashboards, visualizing the summarized view of the sentiment analysis by performing word searches. The model demonstrated high accuracy with Taxi App 1 having 33 positive, 46 negative, and 87 neutral reviews, while Taxi App 2 had 293 positive, 61 negative, and 72 neutral reviews. The model for taxi app 1 had an accuracy of 85.2% and 86.6% for taxi app 2. This proves the reliability and efficiency of the model for service providers In conclusion, this project delivers a robust solution for sentiment analysis, offering service providers valuable insights to enhance their services and improve customer satisfaction. The use of advanced NLP techniques and visualization tools proved to be highly effective, highlighting the model's significance in the online commercial environment.