Youtube_Sentimental_Analysis

We would focus on building a sentiment analysis model for analyzing YouTube reviews using the YouTube Data API.

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Inspiration

My motivation for this hackathon is to learn or get to know how a hackathon actually work

What it does

YouTube sentiment analysis serves as a tool to enhance the platform's content, user experience, safety, and understanding of viewer sentiment and trends. It has various applications benefiting content creators, viewers, YouTube as a platform, researchers, and educators.

How we built it

YouTube Data API: Utilize the YouTube Data API to collect comments information. Train model using the labeled dataset. Fine-tune pre-trained models on your specific data. Model Deployment: Deploy your sentiment analysis model as an API or service using frameworks like Flask.

API Development: Develop API endpoints that accept requests with YouTube video IDs or comment text for analysis.

Challenges we ran into

Accuracy for negative comments were low

What we learned

How to get google client api forf youtube data 3 learnt how to train a model (Sentimental Analysis) Api Integration

What's next for Youtube_Sentimental_Analysis

we are planning to build a good front end with other features like Suggestion from the reviews (Comments) using text analyzes model using gpt api

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