Sentiment Analysis Of Indonesian Civil Servan Candidates 2023 Twitter Network With Naive Bayes Algorithm Method - Harman Akbar Tullah; Muh Akbar; Alem Febri Sonni; Iskandar, Akbar; Erwin Gatot Amiruddin; Kamaruddin; Asnimar (Pusat Penelitian dan Pengabdian Pada Masyarakat Sekolah Tinggi Manajemen Informatika dan Komputer AKBA Makassar, 2023)
The main objective of this research is to uncover the important role played by the social media platform Twitter in shaping public opinion regarding the 2023 Civil Servant Candidate (CPNS) selection process in Indonesia. Using advanced techniques such as social network analysis and Python language processing, as well as the application of the Naive Bayes algorithm, this research carefully examines the conversation patterns and topic trends prevalent on Twitter during the CPNS selection phase. The findings of this research unequivocally highlight the enormous influence of Twitter on public sentiment related to CPNS selection, as demonstrated by the classification model's impressive accuracy rate of approximately 95.19%. In addition, this research successfully identifies the influential roles played by key actors, prominent accounts, and narratives in shaping public perceptions. These groundbreaking insights foster a comprehensive understanding of the dynamic nature of public opinion in the context of CPNS selection, providing an invaluable basis for designing more effective communication strategies for the government and prospective civil servants.