Sentiment analysis plays a pivotal role in comprehending public sentiment and user feedback. In this study, we investigate the effectiveness of feature hashing, a dimensional reduction technique, in conjunction with classifier enhancement to improve sentiment analysis using a movie review dataset under precision, accuracy and recall evaluation metrics. We specifically address two key aspects: the challenges in the movie dataset that necessitated the application of feature hashing and the significant of the propose approach. Our initial assessment of three baseline classifiers: Linear SVC, K-Nearest Neighbors (KNN), and Bernoulli Naive Bayes (NB) revealed precision scores of 85.92%, 61.57%, and 86.52%, respectively. Recognizing that these classifiers might not fully exploit the dataset's potential due to data redundancy issues, we applied feature hashing (FH) to mitigate these challenges. This reduction technique resulted in a streamlined feature set. Following feature hashing, we observed remarkable precision improvements. Linear SVC achieved 90.60%, KNN demonstrated an impressive 92.43%, and Bernoulli (NB) maintained a high precision level of 90.34%. These outcomes shows the effectiveness of feature hashing in eliminating data redundancy, enabling our classifiers to better capture the underlying sentiment nuances in the movie review dataset. Our proposed approach consistently outperforms existing methods, affirming its capacity to enhance sentiment analysis tasks. These findings underscore the significance of dimensional reduction techniques such as feature hashing in bolstering classifier performance. Our study addresses the unique challenges of the movie dataset and highlights the applicability of these techniques in real-world sentiment analysis scenarios, where accurate sentiment assessment is indispensable for informed decision-making and comprehending user sentiments towards products and services.