This project uses the sentiment of public opinoons towards the Super Bowl LVII halftime show using Natual Lenaguage Processing techniques.
Sentiment Analysis has become an increasingly valueble tool across various domains, offerring deeper insights into punlic opinion than ever before. We can use the data and evaluate it with different ML techniques to extract useful insight.
With Sentiment Analyisis we can automate the process of tagging data according to their sentiment, such as positive, negative and neutral. Sentiment allow us to analyze data at scale, detect insights and automate processes.
This project focuses on analyzing the sentiment surrounding the Super Bowl LVII Halftime Show. By analyzing public comments on social media platforms, we aim to understand the overall sentiment towards the show, identify key themes and trends in the conversation, and potentially uncover insights into the audience's preferences.
This project utilizes the YouTube Data API to extract comments and their replies related to the Super Bowl LVII Halftime Show. A script was developed to efficiently retrieve this data, ensuring a comprehensive dataset of public opinion.
The script specifically extracts the video ID of the official NFL channel video for the Super Bowl LVII Halftime Show.
The script is designed to respect user privacy and adhere to YouTube's Terms of Service. It does not collect any personally identifiable information (PII) from the comments, focusing solely on the content itself.