Customer reviews, encompassing both textual descriptions and star ratings, offer valuable insights into customer satisfaction with products and services. While sentiment analysis tools determine whether text is positive, negative, or neutral, their output often differs from the nuanced evaluations conveyed by user-provided star ratings. This mismatch poses a challenge when attempting to correlate sentiment analysis results with traditional star rating systems. This study aims to investigate the correlation between the sentiment in text reviews and their corresponding numeric star ratings. By examining the alignment between these two, we assess whether the sentiment of text reviews accurately resembles the corresponding user-assigned star ratings. Additionally, this research focuses on developing machine learning models to predict star ratings from textual reviews, offering a valuable tool for products or businesses on platforms that lack native rating systems.