Sentiment Analysis of Comments on X Regarding Interactive Videos for Children Using Naive Bayes - Harahap, Lailan Sofinah; Salsabila, Awalia; Nasution, Najwa Fadiyah (Bahasa Indonesia, 2024)
This study aims to analyze the sentiment of social media comments on interactive videos for children using the Naive Bayes algorithm, which is known to be effective in text classification and sentiment analysis. Data were collected from social media platforms regarding comments on popular interactive videos for children, and then processed through cleaning, tokenization, stopwords removal, and stemming stages. Naive Bayes algorithm was used to classify the comments into three categories: positive, neutral, and negative. The analysis showed that 48.3% of the comments were positive, 47.2% were neutral, and 4.5% were negative. Positive sentiments indicated more support for the educational aspects and interactivity, while negative sentiments focused more on content quality and concerns about screen addiction and age appropriateness. The accuracy of the analysis reached 55.6%, which demonstrates the effectiveness of the Naive Bayes algorithm. This research provides useful insights for content developers and policymakers to understand the public's response to interactive children's videos and improve content quality to better suit children's educational and developmental needs