-
-
Video summarization
Inspiration: We started this project to make YouTube video content more understandable. Our goal was to create a faster and more insightful way for users to interact with videos by summarizing the content and performing sentiment analysis.
What It Does: Our project combines various AI techniques to summarize videos and perform sentiment analysis. It extracts and processes video transcripts, making it easier for users to grasp the video's core message. The sentiment analysis offers insights into the emotional context of the content, providing a deeper understanding of the video's impact.
How We Built It: We used the transformers library for sentiment analysis, the youtube_transcript_api for transcript extraction, and trained an LSTM model for sentiment analysis. Our project was developed in Python, employing libraries and tools for natural language processing and deep learning.
Challenges We Faced: We encountered challenges related to data preprocessing, model and tool selection, and optimizing performance. Handling extensive video transcripts and ensuring efficient sentiment analysis posed specific technical hurdles.
Accomplishments We're Proud Of: We successfully established a functional pipeline for YouTube video summarization and sentiment analysis. This combination enhances the YouTube experience, empowering users to make informed decisions about the content they engage with.
What We Learned: This project emphasized the significance of AI in improving content comprehension. It also underscored the potential of natural language processing and deep learning in video content analysis.
Our project demonstrates how AI can create a more efficient, informative, and emotionally resonant YouTube experience.
Built With
- deep-learning
- keras
- lstm
- natural-language-processing
- nltk
- python
- rnn
- scikit-learn
- tensorflow
- transformers
- youtube-transcript-api
Log in or sign up for Devpost to join the conversation.