Trying out NLTK http://madhukaudantha.blogspot.com/2015/05/natural-language-toolkit-nltk-sample.html

nltk-searching.py

from __future__ import division
from nltk.book import *


#Enter their names to find out about these texts
print "===Text Details==="
print text3
#Length of a text from start to finish, in terms of the words and punctuation symbols that appear.
print 'Length of Text: '+str(len(text3))

#Text is just the set of tokens
#print sorted(set(text3))
print 'Length of Token: '+str(len(set(text3)))

#lexical richness of the text
def lexical_richness(text):
    return len(set(text)) / len(text)
    
#percentage of the text is taken up by a specific word    
def percentage(word, text):
    return (100 * text.count(word) / len(text))
    
print 'Lexical richness of the text: '+str(lexical_richness(text3))
print 'Percentage: '+ str(percentage('God',text3)) +'%';

#count the word in the Text
print "===Count==="
print text3.count("Adam")

#'concordance()' view shows us every occurrence of a given word, together with some context.
#Here 'Adam' search in 'The Book of Genesis'
print "===Concordance==="
print text3.concordance("Adam")

#Appending the term similar to the name of the text
print "===Similar==="
print text3.similar("Adam")

#Contexts are shared by two or more words
print "===Common Contexts==="
text3.common_contexts(["Adam", "Noah"])

text3.dispersion_plot(["God","Adam", "Eve", "Noah", "Abram","Sarah", "Joseph", "Shem", "Isaac"])