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tf idf python

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Innocent Iguana answered on February 22, 2022 Popularity 10/10 Helpfulness 3/10

answer tf idf python

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tf idf python

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import nltk
from nltk.corpus import stopwords
# nltk.download('stopwords')
stop_words = set(stopwords.words('english'))

documents = ['cats say meow', 'dogs say woof', 'dogs chase cats']
# Import TfidfVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
# Create a TfidfVectorizer: tfidf
# maximum and minimum fraction a word should occur in is 20% to 80%,  keep top 50 termms
tfidf = TfidfVectorizer(max_df=0.8, min_df=0.2, max_features=50, stop_words=stop_words, ngram_range = (1,1)) # ngram = sequence of words
# Apply fit_transform to document: csr_mat
csr_mat = tfidf.fit_transform(documents)
# Print result of toarray() method
print(csr_mat.toarray())
# Get the words: words
words = tfidf.get_feature_names() # tfidf.get_feature_names_out()
# Print words
print(words)
# Create a dataframe from this sparse matrix representation
df = pd.DataFrame(data=csr_mat.toarray(), columns=words)
# From dataframe to sparse dataframe
sparse_df = some_df.sparse.to_coo()
# From sparse to dense dataframe
dense_df = sparse_df.to_dense()

# see the words and weights in the model
tfidf.vocabulary_ # words as keys and location of the words in the text as value
tfidf[3].indices # word index no from vocabulary that exist on the 4th row
tfidf[3].data # weight of words on the 4th row
# Create a dictionary for each row of data where location of the word in text is key and value is weights
def return_weights(vocab, vector, index):
    zipped = dict(zip(vector[index].indices, vector[index].data))
    return {vocab[i]:zipped[i] for i in vector[index].indices}
print(return_weights(tfidf.vocabulary_, tfidf, 3))

# Clustering on tf-idf
num_clusters = 2  # You can adjust this as needed
cluster_centers, distortion = kmeans(csr_mat.todense(), num_clusters)
df['cluster_labels'], _ = vq(csr_mat.todense(), cluster_centers)
# Display top terms for each cluster
for i in range(num_clusters):
    cluster_df = df[df['cluster_labels'] == i]
    top_terms = cluster_df.mean().sort_values(ascending=False).head().index
    print(top_terms)

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Contributed on Dec 25 2023

Innocent Iguana

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#Importing required module
import numpy as np
from nltk.tokenize import  word_tokenize 
 
#Example text corpus for our tutorial
text = ['Topic sentences are similar to mini thesis statements.\
        Like a thesis statement, a topic sentence has a specific \
        main point. Whereas the thesis is the main point of the essay',\
        'the topic sentence is the main point of the paragraph.\
        Like the thesis statement, a topic sentence has a unifying function. \
        But a thesis statement or topic sentence alone doesn’t guarantee unity.', \
        'An essay is unified if all the paragraphs relate to the thesis,\
        whereas a paragraph is unified if all the sentences relate to the topic sentence.']
 
#Preprocessing the text data
sentences = []
word_set = []
 
for sent in text:
    x = [i.lower() for  i in word_tokenize(sent) if i.isalpha()]
    sentences.append(x)
    for word in x:
        if word not in word_set:
            word_set.append(word)
 
#Set of vocab 
word_set = set(word_set)
#Total documents in our corpus
total_documents = len(sentences)
 
#Creating an index for each word in our vocab.
index_dict = {} #Dictionary to store index for each word
i = 0
for word in word_set:
    index_dict[word] = i
    i += 1

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Contributed on Feb 22 2022

Rias Dwi Prasasti

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python tf idf example

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#Importing required module
import numpy as np
from nltk.tokenize import  word_tokenize 
 
#Example text corpus for our tutorial
text = ['Topic sentences are similar to mini thesis statements.\
        Like a thesis statement, a topic sentence has a specific \
        main point. Whereas the thesis is the main point of the essay',\
        'the topic sentence is the main point of the paragraph.\
        Like the thesis statement, a topic sentence has a unifying function. \
        But a thesis statement or topic sentence alone doesn’t guarantee unity.', \
        'An essay is unified if all the paragraphs relate to the thesis,\
        whereas a paragraph is unified if all the sentences relate to the topic sentence.']
 
#Preprocessing the text data
sentences = []
word_set = []
 
for sent in text:
    x = [i.lower() for  i in word_tokenize(sent) if i.isalpha()]
    sentences.append(x)
    for word in x:
        if word not in word_set:
            word_set.append(word)
 
#Set of vocab 
word_set = set(word_set)
#Total documents in our corpus
total_documents = len(sentences)
 
#Creating an index for each word in our vocab.
index_dict = {} #Dictionary to store index for each word
i = 0
for word in word_set:
    index_dict[word] = i
    i += 1

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Contributed on Aug 11 2022

Safrul M

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TF-IDF

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USAGE: Music Recommender System based on Lyrics or Movie Recommender based
on actor script.
Term Frequency (TF)
-> Number of times a term appears in a document (the count)
e.g. "The man in the Moon" -> "the" has a frequency of 2

Inverse Document Frequency (IDF)
idf = log(N/n) where N is total number of documents
& n is the number of documents that contain a term

TF-IDF ~= TF * IDF

https://marcellodesales.wordpress.com/2009/12/31/tf-idf-in-hadoop-part-1-word-frequency-in-doc/

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Source: marcellodesales.wordpress.com

Tags: tf-idf tf

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Contributed on Sep 15 2023

Nice Narwhal

0 Answers Avg Quality 2/10

tf idf python

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