Sentiment analysis using big data from YouTube videos metadata can be conducted to analyze public opinions on various political figures who represent political parties. This is possible because YouTube has become one of the platforms for people to express themselves, including their opinions on various political figures. The resulting sentiment analysis can be useful for political executives to gain an understanding of public sentiment and develop appropriate and effective political strategies. This study aimed to build a sentiment analysis system leveraging YouTube videos metadata. The sentiment analysis system was built using Apache Kafka, Apache PySpark, and Hadoop for big data handling; TensorFlow for deep learning handling; and FastAPI for deployment on the server. The YouTube videos metadata used in this study is the video description. The sentiment analysis model was built using LSTM algorithm and produces two types of sentiments: positive and negative sentiments. The sentiment analysis results are then visualized in the form a simple web-based dashboard.

利用YouTube视频元数据进行情感分析，以分析各个政党代表的公众意见。该研究旨在构建一个基于Apache Kafka、Apache PySpark和Hadoop处理大数据，TensorFlow处理深度学习，以及FastAPI部署在服务器上的情感分析系统。使用LSTM算法构建情感分析模型，得出积极和消极情感。情感分析结果以一个简单的基于Web的仪表板呈现。

实时分析政治人物：利用YouTube元数据进行情感分析