Update README.md

README.md

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-# Political Campaign Project
-Deep learning pipelines to predict the target of political messages.
-## About
-The goal of this project is to present machine learning approach of classification political campaign videos from the USA of different years by target audience (base/center). The classification is done by extracting different features from the video (e.g., speech-to-text, visual data) and training a neural network. More details can be found in the related [paper](https://drive.google.com/file/d/1-o9UVRRV7XRlGGBsYUfOkmch2ai-A2Fg/view?usp=sharing).
-## Navigation
-### Dataset
-Datasets, including extracted features, tagging files and political campaign videos to train on can be found [here](https://drive.google.com/drive/folders/1-7rkd_SozNGLrNHXnEZ0iTKqO9ztKhiU?usp=sharing).
-### Features extraction
-All the code used for features extraction is in the */tools* directory.
-### Analysis
-Code for model analysis is in the */analysis* directory.
-### Training model
-To train the model use [this](https://colab.research.google.com/drive/1ceVEWRAkIQJsOGuMxmG2qvPY3huZf8gc?usp=sharing) Google Colab notebook. [This](https://colab.research.google.com/drive/1MH19zWCCqQFTKidT5qq6pIPbmsdyuAIp?usp=sharing) notebook is used to make predictions from the pre-trained model.
-### Demo
-Example UI of a pre-trained model with test accuracy of ~80% using speech-to-text and text from video features can be found [here](https://unt2tled-political-campaign-project-demo-6gbfbd.streamlitapp.com/) or by cloning the repository and calling from the project's root:
-```
-pip install streamlit
-streamlit run Demo.py
-```
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