Sentiment Analysis of Using the YouTube Application Using the Naïve Bayes Method

Abstract

This study aims to analyze user sentiment towards the YouTube application using the Naive Bayes method. With the rapid growth of YouTube users worldwide, understanding user preferences and experiences is crucial. Sentiment analysis, a process of processing or extracting textual data to obtain information by categorizing positive or negative sentiment The Naive Bayes algorithm, a statistical approach commonly used in natural language processing and sentiment analysis, was applied due to its simplicity and efficiency. The research involved data collection through web scraping, followed by preprocessing steps such as cleaning, case folding, tokenization, stopword removal, and stemming. Feature selection was performed using TF-IDF (Term Frequency-Inverse Document Frequency) to assign weights to words based on their importance. The Naive Bayes classifier was then trained on the preprocessed data, and its performance was evaluated using accuracy, precision, recall, and F1-score metrics. The results showed an accuracy of 82%, precision of 83%, recall of 98%, and an F1-score of 89%, indicating the effectiveness of the Naive Bayes method in sentiment analysis for the YouTube application. This study provides valuable insights into user sentiment towards YouTube, enabling developers and content creators to enhance user experiences and marketing strategies.