Sentiment Analysis of Application X on The Impact of Social Media Content on Adolescent Mental Well-Being using Naïve Bayes Algorithm

Abstract

Since the pandemic, the use of social media has increased significantly. However, its presence has raised significant concerns about its impact on the mental well-being of teenagers. The pervasive influence of social media has led to substantial changes in the social system within society. Despite this influence, there is currently no comprehensive understanding of the specific impact of social media on mental health. To address this gap, this research proposes the use of sentiment analysis of social media posts with the Naive Bayes algorithm as an approach to identify and classify positive and negative sentiments in these posts related to the mental well-being of teenagers. This solution aims to provide a deeper understanding of the impact of social media content on this vulnerable demographic. In this study, a total of 555,361 social media posts were successfully collected and analyzed using the Naive Bayes algorithm, which was trained with a sample of 27,977 test data. The research results demonstrate that sentiment analysis with the Naive Bayes algorithm is effective in classifying social media sentiment, with 50.55% of the posts classified as positive and 46.97% classified as negative. The identified sentiment patterns have provided valuable insights into the positive and negative impact of social media content on the mental well-being of teenagers.