Technology in the Industrial Revolution 4.0 era supports modern learning through apps like Photomath, simplifying
math problem-solving for users. However, diverse user reviews highlight the need for sentiment analysis to evaluate app quality.
This research analyzes 9,059 reviews of Photomath collected from the Google Play Store using Python. Word2Vec is used in
the study to compare Random Forest and Support Vector Machine (SVM) classifiers for feature extraction. To ensure clean
and consistent data, preprocessing techniques such as stemming, tokenization, and stopword removal were used. Text with rich
semantic aspects was mathematically represented using Word2Vec. The findings show that SVM using an RBF kernel
performed better than Random Forest, with an F1-score of 88.5%, 88.5% accuracy, 88.7% precision, and 88.5% recall.
Performance was effectively improved by combining 300-dimensional Word2Vec with stemming algorithms. While Random
Forest achieved slightly lo