I have a list of objects. Each objects contains longitude, latitude and a list of words.
What I want to do is to predict the location based on the text contained in the object (similar texts should have similar location). Right now I’m using cosine similarity to calculate the similarity between the objects text but I’m stuck how I can use that information to train my neural network. I have a matrix containing each object and how many time each word appeared in that object. F.x. if I had these two objects
Obj C: 54.123, 10.123, [This is a text for object C] Obj B: 57.321, 11.113, [This is a another text for object B]
Then I have something like the following matrix
This is a text for object C another B ObjC: 1 1 1 1 1 1 1 0 0 ObjB: 1 1 1 1 1 1 0 1 1
I would also have something like, for the distance between the two objects (note, that the numbers are not real)
ObjC ObjB ObjC 1 0.25 ObjB 0.25 1
I have looked at how I use neural network to either classify things into groups (like A,B,C) or predict something like a housing price, but nothing that I find helpful for my problem.
I would consider the prediction right if it is within some distance X, since I’m dealing with location. This might be a stupid question, but someone point me to the right direction.