Calculate cosine similarity of all possible text pairs retrieved from 4 mysql tables

Question

I have 4 tables with schema (app, text_id, title, text). Now I'd like to compute the cosine similarity between all possible text pairs (title & text concatenated) and store them eventually in a csv file with fields (app1, app2, text_id1, text1, text_id2, text2, cosine_similarity).

Since there are a lot of possible combinations it should run quite efficient. What is the most common approach here? I'd appreciate any pointers.

Edit: Although the provided reference might touch my problem, I still cant figure out how to approach this. Could someone provide more details on the strategy to accomplish this task? Next to the calculated cosine similarity I need also the corresponding text pairs as an output.

Answer 1

The following is a minimal example to calculate the pairwise cosine similarities between a set of documents (assuming you have successfully retrieved the title and text from your database).

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

# Assume thats the data we have (4 short documents)
data = [
    'I like beer and pizza',
    'I love pizza and pasta',
    'I prefer wine over beer',
    'Thou shalt not pass'
]

# Vectorise the data
vec = TfidfVectorizer()
X = vec.fit_transform(data) # `X` will now be a TF-IDF representation of the data, the first row of `X` corresponds to the first sentence in `data`

# Calculate the pairwise cosine similarities (depending on the amount of data that you are going to have this could take a while)
S = cosine_similarity(X)

'''
S looks as follows:
array([[ 1.        ,  0.4078538 ,  0.19297924,  0.        ],
       [ 0.4078538 ,  1.        ,  0.        ,  0.        ],
       [ 0.19297924,  0.        ,  1.        ,  0.        ],
       [ 0.        ,  0.        ,  0.        ,  1.        ]])

The first row of `S` contains the cosine similarities to every other element in `X`. 
For example the cosine similarity of the first sentence to the third sentence is ~0.193. 
Obviously the similarity of every sentence/document to itself is 1 (hence the diagonal of the sim matrix will be all ones). 
Given that all indices are consistent it is straightforward to extract the corresponding sentences to the similarities.
'''