Social Network Analysis and Sentiment Classification of Robotic Restaurant Content using Naïve Bayes Classifier - Singgalen, Yerik Afrianto (STMIK Budi Darma, 2024)
Sentiment analysis is crucial in understanding public opinion, particularly in emerging technologies such as automation AI and robotic restaurant services. However, achieving accurate sentiment classification in sentiment analysis tasks poses challenges, especially when dealing with imbalanced data. This study employs the Cross-Industry Standard Process for Data Mining (CRISP-DM) through the Naive Bayes Classifier (NBC) algorithm and Synthetic Minority Over-sampling Technique (SMOTE) to address imbalanced data challenges in sentiment analysis. Social network analysis (SNA) collects and analyzes user-generated content related to automation AI and robotic restaurant services, providing insights into public sentiment. Additionally, the occurrence of frequently used words such as