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Terrible Toad answered on March 9, 2022 Popularity 1/10 Helpfulness 1/10

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d1="the best Italian restaurant enjoy the best pasta"
d2="american restaurant enjoy the best hamburger"

pip install textmining3

#termdocumentmatrix
import textmining
tdm=textmining.TermDocumentMatrix()
tdm.add_doc(d1)
tdm.add_doc(d2)
tdm=tdm.to_df(cutoff=0)
tdm

#inverted index
docs=[d1,d2]
inverted_index={}
for i,doc in enumerate(docs):
  for term in doc.split():
    if term in inverted_index:
      inverted_index[term].add(i)
    else:
      inverted_index[term]={i}

inverted_index

#tfidf
from sklearn.feature_extraction.text import TfidfVectorizer
tfidf=TfidfVectorizer()
result=tfidf.fit_transform(docs)
print(tfidf.vocabulary_)
print(result)

#cosine similarity
from sklearn.metrics.pairwise import cosine_similarity
print(cosine_similarity(tdm))

Popularity 1/10 Helpfulness 1/10 Language whatever

Source: Grepper

Tags: exp whatever

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Contributed on Mar 09 2022

Terrible Toad

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