Selling vegetables through live streaming: sentiment and network analysis - Yerik Afrianto Singgalen (Institute of Computer Science (IOCS), 2024)
This study addresses the research problem of understanding digital interactions and dynamics in online environments, mainly focusing on sentiment analysis and Social Network Analysis (SNA). The methodology integrates sentiment analysis techniques to discern prevailing attitudes and emotions within digital content, coupled with SNA to unveil intricate network structures and user relationships. Concurrently, SNA unveils intricate network structures and relationships among users, illuminated by numerical metrics such as Diameter (2), Density (0.003982), Reciprocity (0.000000), Centralization (0.027240), and Modularity (0.978600). Additionally, the performance vector further enhances the evaluation with metrics including accuracy (97.68% +/- 2.44%), AUC (0.429 +/- 0.477), precision (97.68% +/- 2.44%), recall (100.00% +/- 0.00%), and f-measure (98.81% +/- 1.25%). The study utilizes a dataset of digital content and user interactions, applying sentiment analysis to quantify sentiments and SNA to map network connections. Findings reveal nuanced insights into audience perceptions, engagement patterns, and network dynamics within digital ecosystems. Moreover, the study employs numerical metrics to evaluate the performance of sentiment analysis and SNA methodologies. The results underscore the importance of integrating sentiment analysis and SNA in comprehensively understanding online behavior and communication dynamics, offering valuable insights for content creation, engagement optimization, and community management strategies in digital environments.