Public Sentiment Analysis on the Boycott Israel Movement on Platform X Using Random Forest and Logistic Regression Algorithms - Agustin, Rachmayanti Tri; Cahyana, Yana; Baihaqi, Kiki Ahmad; Rohana, Tatang (Journal of Applied Informatics and Computing, 2025)
This research aims to analyze public sentiment toward the boycott movement against Israel on the X platform by applying Random Forest and Logistic Regression algorithms. The study uses 616 tweets collected through web crawling with relevant keywords such as "Boikot", "Israel", and "Palestine", covering the period from March 1, 2023 to January 30, 2025. The dataset underwent preprocessing including cleaning, normalization, stopword removal, tokenization, and stemming. Sentiment labeling was conducted both manually, categorizing the data into positive, negative, and neutral classes. TF-IDF was used for feature weighting. The data was split into 80% training and 20% testing. The Random Forest model achieved an accuracy of 70%, while Logistic Regression reached 68%. Both models showed higher accuracy in predicting positive sentiment compared to negative and neutral. The results suggest that public opinion on the boycott movement on social media tends to be supportive, with “Boikot,” “Israel,” and “Palestine” being the most dominant terms. Random Forest performed slightly better in classification, though improvements are needed in recognizing non-positive sentiments.