I would like to find the most relevant words over a set of documents.
I would like to call a Tf Idf algorithm over 3 documents and return a csv file containing each word and its frequency.
After that, I will take only the ones with a high number and I will use them.
I found this implementation that does what I need https://github.com/mccurdyc/tf-idf/.
I call that jar using the subprocess library. But there is a huge problem in that code: it commits a lot of mistake in analyzing words. It mixs some words, it has problems with ' and - (I think). I am using it over the text of 3 books (Harry Potter) and , for example, I am obtaining words such hermiones, hermionell, riddlehermione, thinghermione instead of just hermione in the csv file.
Am I doing wrong something? Can you give me a working implementation of the Tf idf algorithm? Is there a python library that does that?
Here is an implementation of the Tf-idf algorithm using scikit-learn.
Before applying it, you can word_tokenize() and stem your words.
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from nltk import word_tokenize
from nltk.stem.porter import PorterStemmer
def tokenize(text):
tokens = word_tokenize(text)
stems = []
for item in tokens: stems.append(PorterStemmer().stem(item))
return stems
# your corpus
text = ["This is your first text book", "This is the third text for analysis", "This is another text"]
# word tokenize and stem
text = [" ".join(tokenize(txt.lower())) for txt in text]
vectorizer = TfidfVectorizer()
matrix = vectorizer.fit_transform(text).todense()
# transform the matrix to a pandas df
matrix = pd.DataFrame(matrix, columns=vectorizer.get_feature_names())
# sum over each document (axis=0)
top_words = matrix.sum(axis=0).sort_values(ascending=False)
