# Text topic classification in tensorflow

I want to create a CNN in tensorflow that does the following: Classify a recipe headline and find out the topic. For instance super yummy cheesy cake should result in cheese cake and so on.

I thought for going with tensorflow, but need some help in getting things started.

## My strategy is like that:

1. Normalize the headlines so cheesy becomes cheese and cheesecake becomes cheese cake for instance and so on.
2. Having a dataset like:

• super yummy cheesecake | cheese cake
• summer strawberry cake | strawberry cake
3. Train the model to learn what matters for the topic and what is just additional information.

The way, the dataset is modeled, I have no static lables, as I understand. This makes things complicated, right?

As this is my first AI experiment with tensorflow, I don't really know if this will work out, or if I should go with another strategy, therefore I need your help.

• One question: why a CNN? The best models to process text data are RNNs, could you elaborate more on your choice? (Not a criticism, I'm genuinely interested!) Jan 3, 2020 at 9:12