Spaces:
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title: Social-Stat | |
emoji: 🦕 | |
colorFrom: indigo | |
colorTo: pink | |
sdk: streamlit | |
sdk_version: 1.29.0 | |
app_file: src/app.py | |
pinned: false | |
# Social-Stat: A Streamlit Web App for Social Media Analysis | |
Streamlit web application for social network analysis. | |
[Hugging Face Spaces](https://huggingface.co/spaces/molokhovdmitry/social-stat) | |
![social-stat](social-stat.gif) | |
## Features | |
- **Emotion Prediction**: Utilizes a text classification model to predict emotions in video comments. | |
- **Topic Modeling**: Applies Non-negative Matrix Factorization (NMF) to identify and visualize the main topics discussed in the comments. | |
- **t-SNE Visualization**: Provides a 2D and 3D visualization of the comment data, highlighting patterns and clusters. | |
- **Language Detection**: Detects the language of comments to understand the global reach of the video and visualizes the distribution of languages on a **plotly Choropleth** map. | |
## How to Use | |
1. **Enter a YouTube Video URL or ID**: Input the URL or ID of the YouTube video. | |
2. **Select Analysis Options**: Choose whether to predict emotions, perform NMF, visualize with t-SNE, and display a language map. | |
3. **Adjust Parameters**: Customize the analysis by adjusting parameters such as the number of NMF components, t-SNE perplexity. | |
4. **Analyze**: Click the "Analyze" button to start the analysis. | |
# Installation and Running | |
``` | |
git clone https://github.com/molokhovdmitry/social-stat | |
python -m pip install --upgrade pip | |
pip install -r requirements.txt | |
streamlit run src/app.py | |
``` | |