Analisis Sentimen Masyarakat Terhadap Tiktok Shop di Twitter Menggunakan Metode Naive Bayes Classifier - Andrian, Eka; Isnain, Auliya Rahman (JURNAL MEDIA INFORMATIKA BUDIDARMA, 2024)
This research aims to analyze public sentiment towards TikTok Shop through the Twitter platform using the Naive Bayes Classifier Algorithm. This algorithm is used to evaluate public views regarding TikTok Shop and identify Positive and Negative sentiments. The data used in this research is 3,816 data. Then, there are Positive sentiment results of 53.45% and Negative of 46.55%. After analyzing the data, the accuracy result is 78.22% using the Split Data operator. After that, for the results of the Naïve Bayes Classifier implementation on the Recall value has a result of 84% and for the class precision result of 86%. The purpose of this research is to evaluate public views on TikTok Shop through the Twitter platform by utilizing the Naive Bayes Classifier Algorithm. This algorithm is used to analyze sentiments that arise regarding TikTok Shop, with a focus on identifying whether the sentiment is Positive or Negative. This analysis is also used to find out different public opinions about TikTok Shop, such as user experience, features used, and impacts experienced. Therefore, sentiment analysis and natural data processing use the Python programming language to categorize user comment data through a splitting process.