50.021 AI FNC-1

Prerequisites:

• Python 3.x
• Access to a terminal or command-line interface

Initial Setup:

1. Clone the Repository:

   • Begin by cloning the project repository from GitHub to your local machine:

   • git clone https://github.com/liangjunyi010/50.021_AI.git

   • Or, unzip our code folder in our submitted compressed file.

2. Install Required Packages:

   • Navigate to the root directory of the project and install the required Python packages using the following command:

   • pip install -r requirements.txt

3. Download Additional Resources:

   • Execute the following command to download the VADER lexicon for sentiment analysis:

   • python -m nltk.downloader vader_lexicon

   • Download the pre-trained Google Word2Vec model from the following link: https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit?pli=1&resourcekey=0-wjGZdNAUop6WykTtMip30g After downloading, extract the .bin file and place it in the google_model directory within your project.

Configuring Paths:

Update Model Path in predict.py

Modify the path in predict.py to point to the best_model.joblib file within your project's model directory. Replace the existing path with your absolute path.

model = load('path_to_project_root/model/best_model.joblib')

Update Word2Vec Model Path:

In the file word_to_vec_feature_generator.py within the feature_extractor directory, change the model_path parameter in the init method to the absolute path of the GoogleNews-vectors-negative300.bin file.

def __init__(self, model_path='path_to_project_root/google_model/GoogleNews-vectors-negative300.bin'):

Running the Application:

Database Migrations:

Make sure you are in the djangoproject directory and execute the following commands to prepare the database:

python manage.py makemigrations
python manage.py migrate

Start the Server:

Run the Django development server using the command:

python manage.py runserver 8000

Access the Application:

Open a web browser and go to http://127.0.0.1:8000/ to view and interact with the GUI.

Updating the Model:

Re-train the Model (Optional):

If you wish to re-train the model with new data or tweaks, navigate back to the project root directory (50.021_AI) and execute the following command:

python fnc_kfold.py

Wait for the process to complete before attempting to use the updated model.

After following these steps, your News Relationship Classifier should be up and running, ready for you to analyze the relationship between news headlines and their corresponding body text.