kyroha...@gmail.com wrote: > This is another account but I am the op. Why do you mean normalize? Sorry > I’m new at this.
Take three texts containing the words covid, vaccine, program, python Some preparatory imports because I'm using numpy: >>> from numpy import array >>> from numpy.linalg import norm The texts as vectors, the first entry representing "covid" etc.: >>> text1 = array([1, 1, 0, 0]) # a short text about health >>> text2 = array([5, 5, 0, 0]) # a longer text about health >>> text3 = array([0, 0, 1, 1]) # a short text about programming in Python Using your distance algorithm you get >>> norm(text1-text2) 5.6568542494923806 >>> norm(text1-text3) 2.0 The two short texts have greater similarity than the texts about the same topic! You get a better result if you divide by the total number of words, i. e. replace absolute word count with relative word frequency >>> text1/text1.sum() array([ 0.5, 0.5, 0. , 0. ]) >>> norm(text1/text1.sum() - text2/text2.sum()) 0.0 >>> norm(text1/text1.sum() - text3/text3.sum()) 1.0 or normalize the vector length: >>> norm(text1/norm(text1) - text2/norm(text2)) 0.0 >>> norm(text1/norm(text1) - text3/norm(text3)) 1.4142135623730949 -- https://mail.python.org/mailman/listinfo/python-list
