Sentiment Analysis of Zoom Application Using Support Vector Machine

This research investigates the academic community's perceptions of Zoom, a leading digital education platform that has become integral to learning in the modern era. Through a meticulous sentiment analysis conducted using the Support Vector Machine method, this study analyzes an extensive dataset of 5000 user comments sourced from the Play Store. The SVM method's robustness is demonstrated by its ability to classify sentiments with high accuracy, achieving an impressive accuracy rate of 88.34%. The precision rate stands at 85.17%, indicating the reliability of the results, while the recall rate of 85.30% reflects the method's effectiveness in identifying relevant data points. Furthermore, an F1-score of 85.22% signifies the balanced harmony between precision and recall, underscoring the method's overall efficacy. These metrics collectively highlight the societal sentiments towards Zoom as an educational tool and underscore its pivotal role in digital learning. The insights from this analysis are significant and have practical implications for enhancing user engagement and satisfaction with digital education platforms.