ANALISIS SENTIMEN ULASAN PENGGUNA APLIKASI PINTEREST PADA GOOGLE PLAY STORE MENGGUNAKAN ALGORITMA NAIVE BAYES CLASSIFIER - Muh Arfah Wahlil Pratama; Suryadi Suryadi; Haldi Alfaisal; Nurjaya Nurjaya; Nia rahmadani Nia; Ahmad Ashar; Elsa Elsa; Riksal Rivaldi; Sri Rejeki; Dimas Rezkianto; Iin Sugiarti; Zulfikar Isnansah (Yayasan Pendidikan Cahaya Budaya Indonesia, 2026)
The rapid growth of user reviews on the Google Play Store offers valuable insights for app developers. This study conducts sentiment analysis on Pinterest user reviews using the Naive Bayes Classifier algorithm. Data was collected via web scraping using the `google-play-scraper` library within the Google Colaboratory environment. The dataset, consisting of Indonesian-language reviews, underwent preprocessing steps including case folding, tokenization, stopword removal, and stemming. Feature extraction was performed using TF-IDF, and the model was evaluated using a confusion matrix with an 80:20 train-test split. The results indicate that the Naive Bayes model achieved an accuracy of 84.00%, a precision of 84.00%, and a recall of 77.78%. The sentiment distribution reveals a predominance of positive reviews, reflecting overall user satisfaction with the Pinterest app. This research contributes to the understanding of public opinion regarding visual-based applications and validates the effectiveness of Google Colab as an integrated platform for Indonesian-language sentiment analysis.