Analyzing Political Figures in Real-Time: Leveraging YouTube Metadata
  for Sentiment Analysis
Danendra Athallariq Harya Putra Arief Purnama Muharram
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.