DeepEngage is a engagement prediction tool for posts within specific instagram niches. An implementation for food-related posts can be found here
The final product accepts a group of images (simulated through preset groups in the demo) and the sponsored content creator's username, then selects the post with the greatest engagement potential. The number overlayed on each image represents how much engagement it is forecasted to receive relative to the average (e.g. a score of 1.2 is predicted to get 1200 likes if the average for that user is 1000). The "Recommended Optimizations" pane applies a series of filters to the chosen image and recommends changes if they are expected to perform better than the original.
The core packages required are:
FastAI V1 (conda install -c fastai fastai)
ImageIO (conda install -c conda-forge imageio)
Selenium (conda install -c conda-forge selenium)
Note: there is currently an incompatibility with Pillow and PyTorch, which requires you to downgrade Pillow (conda install pillow=6.1)
Selenium also requires that the path have access to chromedriver.exe, which can be downloaded from here
Models are trained using images and tabular data scraped from Instagram. This process is outlined in the "Web Scraping Pipeline.ipynb" notebook in the main directory
Model training can be performed using the "Model Training and Prediction.ipynb" notebook.
Performance of models can be visualized and explored in the "Post_Processing_Exploration.ipynb" notebook