With the continuous growth of user-generated data on social media
platforms, its analysis requires a deep understanding of sentiment trends. Data from
social sites is collected using web scraping techniques and then filtered for consistency
through pre-processing methods. In this study, multiple techniques are applied to
analyze YouTube comments. The BERT transformer model is used to classify
comments into positive, negative, and neutral categories, enabling sentiment analysis
and providing insights into user opinions and trends. Additionally, Latent Dirichlet
Allocation (LDA) is employed for thematic analysis to identify key discussion topics
within the comments. The performance of the proposed approach is evaluated using the
F1-score metric. For future improvements, deep learning techniques could be explored
to enhance the accuracy of sentiment analysis.
Keywords: BERT method, LDA method, Sentiment analysis, Thematic analysis, Web scraping.
