Okay so I am a little confused. This should be a simple straightforward question however.

After calculating the TF-IDF Matrix of the Document against the entire corpus, I get a result very similar to this:

array([[ 0.85...,  0.  ...,  0.52...],
       [ 1.  ...,  0.  ...,  0.  ...],
       [ 1.  ...,  0.  ...,  0.  ...],
       [ 1.  ...,  0.  ...,  0.  ...],
       [ 0.55...,  0.83...,  0.  ...],
       [ 0.63...,  0.  ...,  0.77...]])

How do I use this result to get the most similar document against the search query? Basically I am trying to re-create a search bar for Wikipedia. Based on a search query I want to return the most relevant articles from Wikipedia. In this scenario, there are 6 articles (rows) and the search query contains 3 words (columns).

Do I add up all the results in the columns or add up all the rows? Is the greater value the most relevant or is the lowest value the most relevant?

python nlp nltk scikit-learn tf-idf
Edit
Report