Analysis Netizen Sentiment towards Corporate Social Responsibility (CSR) of Bank Syariah Indonesia (BSI) on Social Media Using Machine Learning Algorithms - Nur Laily Hidayati; Jafar Shiddiq; Muhammad Khoirun Nasirin (LPPM STIES BABUSSALAM, 2026)
The development of social media has change method public assess and respond implementation of Corporate Social Responsibility (CSR) by institutions financial sector , including Bank Syariah Indonesia (BSI). Opinions expressed by netizens through various social media platforms reflect level acceptance , satisfaction , and expectation public on the CSR programs being implemented . Research This aim For analyze netizen sentiment towards Implementation of CSR of Bank Syariah Indonesia (BSI) on social media use algorithm machine learning , at the same time identify most topics​ get attention public . Research use approach quantitative with method analysis sentiment based text mining . Data obtained through the web scraping process from social media platforms that contain keywords related to BSI CSR during period observation certain . Stages study covering data collection , pre-processing text (case folding, tokenization, stopword removal, stemming), labeling sentiment , extraction feature use Term Frequency-Inverse Document Frequency (TF-IDF), as well as classification use algorithm Support Vector Machine (SVM), Naïve Bayes , and Random Forest . Model performance was evaluated use metric accuracy , precision , recall , and F1-score . Research results show that majority netizen sentiment towards BSI's CSR program is positive by 68.4%, followed by sentiment neutral by 18.7%, and sentiment negative by 12.9%. The SVM algorithm provides performance best with level accuracy of 92.4%, surpassing Naïve Bayes (88.6%) and Random Forest (90.8%) . Keyword analysis show that issue education , empowerment of MSMEs, assistance social , conservation environment and sustainability become the most topics appreciated public . On the contrary , sentiment negative more Lots triggered by perception about lack of program equity , transparency reporting , and effectiveness implementation in several regions. Findings study show that analysis sentiment based machine learning capable become instrument effective evaluation​ in measure perception public in real-time, so that can support taking decision BSI 's strategic increase quality , transparency , and sustainability of aligned CSR programs with principle maqashid sharia and sustainable finance