AdCategorization Model

Categorizes ads based on text content

Tools Used

• Python 3.11.5

Input

Text content

Output

Category of the text (post)

Training model locally

1. Clone the repository
2. Create a virtual env and run

pip install -r requirement.txt

3. Download the GoogleNews-vectors-negative300.bin.gz data file and keep it on the root directory of the project.
4. You can run the jupyter notebook trainingModel.ipynb file on any supporting editor or jupyter notebook itself.

Running the API

To run the API locally, you need to run the flask server using command:

python main.py

License

A comparative study of text categorization for advertisement classification © 2019 by Subash Chandra Sapkota is licensed under CC BY 4.0. To view a copy of this license, visit LICENSE file.