When it comes to rank, in your application, you don't need to have missing values. When a word has an occurrence in one file but not in the other, you can give it last ranking in the other file (or equal last ranking for multiple missing values).
However, I am not sure of the effect on the Spearman value of lots of missing values (lots of tied last ranks). You might instead consider using a standard correlation/regression on the raw relative frequencies, instead of the Spearman coefficient.
Example...
Say file x has m=113 words and file y has n=234. We can create a table of relative word frequencies like so:
word x y
is 5/113 23/234
the 4/113 45/234
a 4/113 17/234
farnarkling 1/113 0/234
elbow 0/113 2/234
...
===============================
TOTAL 113/113 234/234
You would then calculate:
word x y u=x*y v=x*x
is 5/113 23/234 115/26442 25/12769
the 4/113 45/234 180/26442 16/12769
a 4/113 17/234 68/26442 16/12769
farnarkling 1/113 0/234 0/26442 1/12769
elbow 0/113 2/234 0/26442 0/12769
...
========================================================
TOTAL 113/113 234/234 s=(sum of u) t=(sum of v)
Your answer is given by s/t. A value close to m/n implies a good correspondence.
Some possibly useful links are:
https://statistics.laerd.com/statistical-guides/spearmans-rank-order-correlation-statistical-guide.php
http://en.wikipedia.org/wiki/Simple_linear_regression