Collaboration of Sentiment Analysis and Social Network Analysis Methods in Supporting Digital Talent Development in Indonesia

This research analyses the development of digital talent as a key driver of technological and economic growth in Indonesia. The challenge of understanding public sentiment and identifying key influencing factors is addressed by integrating Sentiment Analysis and Social Network Analysis (SNA). Using Support Vector Machine (SVM) with Term Frequency and Inverse Document Frequency (TF-IDF), analysis sentiment was classified with an accuracy rate of 94.60%. SNA identified @kemkominfo and @kempanrb as influential actors. The integration of these methods provides a comprehensive understanding of sentiment trends and network influence, allowing stakeholders to optimize engagement strategies and effectively reach key stakeholders. This approach highlights the value of combining sentiment and network analysis to support evidence-based strategies that can accelerate digital talent development in Indonesia.