Inspiration
- link - How to Extract all YouTube Comments and Comment Replies from a Playlist (Analytics with Adam) - this video inspired me as it gave a solution regarding extracting comments from YouTube and saving them to a CSV file
- link - NLP project : YouTube comments sentiment analysis ft.Blackadam⚡ (AK Python) - this video helped me learn how to do sentiment analysis
What it does
The theme of my project is ML + Language. This sentiment analysis app makes it possible for users to enter the link to a public YouTube playlist and get data about the general sentiment of it. Users can also get the most liked comments, which are important because they reflect popular opinions and provide valuable insights into the community's preferences and sentiments. In order to get all of this information, users must enter their YouTube API key and the link to the playlist they want to analyze.
How we built it
To develop this sentiment analysis app, I leveraged the power of Python along with Streamlit for creating the user interface and NLTK (Natural Language Toolkit) for processing and analyzing the textual data. The first step involved utilizing the YouTube Data API, for which users need to input their own YouTube API key. This key is crucial for accessing YouTube data, including details about the specified playlist. For the user interface, I chose Streamlit due to its simplicity and effectiveness in creating interactive web applications with minimal code. Streamlit allowed me to design a user-friendly interface where users can easily input their YouTube API key and playlist link. The core functionality of sentiment analysis was implemented using the Natural Language Toolkit (NLTK). NLTK provides powerful tools for processing human language data and is widely used for tasks like sentiment analysis. In addition to sentiment analysis, the app extracts the most liked comments from the specified playlist. This is achieved by fetching the comments, sorting them based on the number of likes, and presenting the top comments to the user. These comments serve as valuable insights into the sentiments and preferences of the community.
Challenges we ran into
Dealing with diverse languages, slang, and informal expressions in YouTube comments expanded my understanding of natural language processing challenges. Working with the YouTube Data API comes with quota restrictions and rate limits. Managing API quotas effectively to handle a large number of requests posed a significant challenge.
Accomplishments that we're proud of
I am proud of the way the application flows smoothly due to my effort in making it this way. Users can access the sentiment analysis features effortlessly, entering their YouTube API key and playlist link. I am also proud of the progress bar that I made, which appears while the YouTube comments are fetched. This feature not only enhances the visual appeal of the application but also provides users with real-time feedback on the progress of the comments retrieval process.
What we learned
Dealing with diverse languages, slang, and informal expressions in YouTube comments expanded my understanding of natural language processing challenges. Exploring Streamlit for creating web interfaces enhanced my proficiency in developing user-friendly applications with minimal effort.
What's next for YouTube Playlist Sentiment Analysis
To make the app more inclusive, incorporating support for a wider range of languages is essential. This expansion will enable users from diverse linguistic backgrounds to analyze sentiments effectively, fostering a global user community.
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