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.

翻译：利用YouTube视频元数据中的大数据进行情感分析，可分析公众对各政党代表性政治人物的舆论倾向。这一可行性源于YouTube已成为公众表达对政治人物看法的平台之一。所得情感分析结果有助于政治决策者把握公众情绪，制定恰当有效的政治策略。本研究旨在构建基于YouTube视频元数据的情感分析系统。该系统采用Apache Kafka、Apache PySpark和Hadoop处理大数据，TensorFlow处理深度学习任务，FastAPI进行服务器端部署。研究使用的YouTube视频元数据为视频描述文本。情感分析模型基于LSTM算法构建，输出正面与负面两种情感倾向。分析结果最终以简易网页仪表盘形式可视化呈现。