Sentiment Towards Social Media Politeness Ambassadors: A Case Study Using the Naive Bayes Method - Fikri, Ridho Ahmad; Muliono, Rizki (JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING, 2025)
Social media has had a significant impact on modern society, serving as a primary platform for sharing information and opinions. One intriguing phenomenon is the viral case of a female police officer, Putri Cikita, who earned the title "Ambassador of Courtesy" due to her actions in a video. This study aims to analyze public sentiment regarding this case on Twitter using the Naive Bayes Classifier (NBC) method. The research adopts a quantitative descriptive approach with sentiment analysis based on Text Mining, utilizing Python and Google Colab. The dataset consists of 2,000 Indonesian-language tweets collected from August to November 2024 using the keywords "Ambassador of Courtesy" and "Putri Cikita." The research stages include data collection, data preprocessing (case folding, tokenizing, filtering, stemming), and sentiment labeling into positive, negative, and neutral classes. The analysis results reveal that 11.55% of tweets express positive sentiment, 68.40% are neutral, and 20.05% are negative. The Naive Bayes method proves effective in classifying textual sentiment data. This research provides insights into public perceptions of viral events and underscores the importance of public image management in the digital era.