I have a terror attack (tabular) data set. Each row is one attack and there are columns like:
- Date of the attack (daily resolution)
- Location of the attack (long/lat, as well as city/country)
- Number of casualties
- Attack/weapon type
- A few boolean columns like whether it was a suicide attack
Furthermore, I have a text column that holds a 2-3 sentence description of the attack. This is the main column I want to use for training/predicting.
There are several target columns of the form "is_left_wing", "is_right_wing", etc. The values are 0, 1, and -1. Here 0 means the attack didn't have the respective motive, 1 means it had the motive and -1 means it is unknown.
In short, my goal is to build a model that is trained on the 0 and 1 values in the target columns and makes predictions about the -1s.
The main thing I'm stuck on is how to extract features from the text column with the attack description. I have limited NLP experience and I want to use something more sophisticated than a simple bag of words model.
I would appreciate suggestions about the general approach to this problem (also some good readings on the topic).