YouTube Video Sentiment

Introduction

In today's digital age, YouTube has become a major platform for sharing opinions, experiences, and information. With millions of videos uploaded daily, understanding the sentiment expressed in these videos can provide valuable insights for various stakeholders, from marketers to social scientists. However, analyzing the sentiment of video content poses unique challenges, particularly when dealing with the audio component.
This project aims to tackle these challenges by developing a comprehensive approach to YouTube video sentiment analysis. By leveraging state-of-the-art tools and technologies, we extract audio from YouTube videos, transcribe the audio into text, and classify the sentiment of the transcribed text. The process involves using Pythonfixtube for audio extraction, OpenAI Whisper for transcription, and fine-tuning a BERTSequenceClassifier for sentiment classification. The final model is deployed on Hugging Face, making it accessible for broader use and evaluation.
Approach wads not only automates the sentiment analysis process but also provides a scalable solution that can be applied to datasets of YouTube videos. This project showcases the power of combining modern AI techniques with practical applications, offering a valuable tool for sentiment analysis in the digital content space.

Project Overview

1. Fetching Data

• Start Here: The initial step in the process.
• Fetching Audio: Extract audio from YouTube videos using Pythonfix tube.

2. Preparing Data

• Using OpenAI Whisper: Transcribe the audio to text.
• Cleaning + Preprocessing: Process the transcribed text for further analysis.
• Converting to Sentences + Tokenization: Prepare the text data by breaking it into sentences and tokenizing it.

3. Model Training

• BERT: Utilize the BERT model for sentiment analysis.
• Fine-Tuning: Fine-tune the BERT model on the prepared data.
• Performance Metrics: Evaluate the model's performance using various metrics.

4. Deploying Model

• Gradio: Utilize Gradio for creating a user-friendly interface.
• Hugging Face: Deploy the model on Hugging Face for broader accessibility.

Data Collection and Preparation

1.Collecting Video Links

• The first step in the project was to gather links to YouTube videos that represented both positive and negative sentiments. These links were curated based on the content and context of the videos to ensure a balanced dataset.

2.Storing Links

• The collected video links were stored in a text file, with separate files for positive and negative sentiment videos. This organization facilitated the subsequent data processing steps.

3.Downloading Audio

• Using the Pythonfix tube tool, the audio from each video was downloaded. The tool was configured to save the audio files into respective folders based on their sentiment category (positive or negative). This organization helped maintain clarity and ease of access for further processing.

Audio Transcription

4.Transcribing Audio to Text

• After downloading the audio files, the next step was to transcribe the audio into text. For this, we used OpenAI Whisper, a powerful tool for converting spoken language into written text.
• Each audio file was processed, and the resulting text was stored in the respective folders based on their sentiment category (positive or negative). This structured approach ensured that the transcriptions were organized and easily accessible for the next stages of the project.

5.Data Augmentation and Processing

• To enhance the robustness of the sentiment analysis model, we supplemented the transcriptions from the YouTube videos with another well-curated dataset optimized for sentiment classification. This additional dataset helped provide a broader range of sentiment examples.
• The two datasets were merged and processed to create a combined dataframe. This step involved cleaning the text data, removing noise, and ensuring consistency in formatting. The data was then tokenized and converted into a suitable format for training the model. ## 6.Fine-Tuning the BERT Model
• The combined dataset served as the training data for fine-tuning a BERT model. BERT (Bidirectional Encoder Representations from Transformers) is a state-of-the-art model for natural language understanding tasks.
• The fine-tuning process involved adjusting the pre-trained BERT model to the specific nuances of our sentiment classification task. The model was trained on the combined dataset, optimizing it to accurately classify the sentiment of the text data into positive or negative categories. User Interface and Deployment
• Creating a User-Friendly Interface o To make the sentiment analysis tool accessible to a broader audience, we developed a user-friendly interface using Gradio. Gradio is an open-source library that simplifies the creation of web-based interfaces for machine learning models. o The interface allows users to input a YouTube video URL, and the tool automatically extracts the audio, transcribes it, and predicts the sentiment of the video. This streamlined process makes it easy for users to analyze the sentiment of YouTube videos without requiring technical expertise. ## 8.Deployment on Hugging Face
• The final model, along with the Gradio interface, was deployed on Hugging Face. Hugging Face provides a platform for hosting and sharing machine learning models, making them accessible to the community.
• By deploying the model on Hugging Face, we ensured that it is easily accessible for anyone to use and experiment with, further expanding its utility and reach.

Conclusion

The YouTube Video Sentiment Analysis project showcases the potential of combining advanced AI technologies with practical applications. By leveraging tools like Pythonfix tube for audio extraction, OpenAI Whisper for transcription, and BERT for sentiment analysis, we developed a robust system capable of analyzing the sentiment of YouTube videos. The integration of a user-friendly interface through Gradio and deployment on Hugging Face further enhances the accessibility and usability of the tool.
Here is hugging Face APP: https://huggingface.co/spaces/Saim-11/Youtube-Videos-Sentiment