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WordCloud.process_text vs sklearn's CountVectorizer

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0

I would like to count the term frequency across the corpus. To do that, there are two ways, which was using CountVectorizer and sum in axis=0 as below.

count_vec = CountVectorizer(tokenizer=cab_tokenizer, ngram_range=(1,2), stop_words=stopwords)
cv_X = count_vec.fit_transform(string_list)

Another way is using WordCloud.process_text() (see doc here) which will result in term-frequency dict. I used stopword from previously TfIdfVectorizer using tfidf_vec.get_stop_words().

text_freq = WordCloud(stopwords=stopwords, collocations=True).process_text(text)

The fact that I am using stopwords from the TfIdfVectorizer, I am expecting this to behave the same, however, the features/terms I am getting is different (length of the dict is less than TfIdfVectorizer.get_feature_names().

So, I am wondering, what is the different of using one over another? Is one more accurate than the other?

python python-3.x scikit-learn word-cloud countvectorizer

share|improve this question

asked Mar 8 at 4:28

Darren ChristopherDarren Christopher

427315

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  1
  

  
  
  
  
  
  

  
  
  I see 2 reasons tokens from both methods are different: (1) cab_tokenizer and (2) ngram_range. You may feed a simple, several-words long string to both classes and see how the output would be different.
  
  – Sergey Bushmanov
  Mar 8 at 6:35
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Ah yes, you are right, I also add lemmatizer in cab_tokenizer so it could be the reason. The ngram_range=(1,2) means it analyse up to bigram, which is identical with collocations=True on WordCloud.
  
  – Darren Christopher
  Mar 8 at 7:00
  

  
  
  
  

add a comment | 

0

I would like to count the term frequency across the corpus. To do that, there are two ways, which was using CountVectorizer and sum in axis=0 as below.

count_vec = CountVectorizer(tokenizer=cab_tokenizer, ngram_range=(1,2), stop_words=stopwords)
cv_X = count_vec.fit_transform(string_list)

Another way is using WordCloud.process_text() (see doc here) which will result in term-frequency dict. I used stopword from previously TfIdfVectorizer using tfidf_vec.get_stop_words().

text_freq = WordCloud(stopwords=stopwords, collocations=True).process_text(text)

The fact that I am using stopwords from the TfIdfVectorizer, I am expecting this to behave the same, however, the features/terms I am getting is different (length of the dict is less than TfIdfVectorizer.get_feature_names().

So, I am wondering, what is the different of using one over another? Is one more accurate than the other?

python python-3.x scikit-learn word-cloud countvectorizer

share|improve this question

asked Mar 8 at 4:28

Darren ChristopherDarren Christopher

427315

• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  I see 2 reasons tokens from both methods are different: (1) cab_tokenizer and (2) ngram_range. You may feed a simple, several-words long string to both classes and see how the output would be different.
  
  – Sergey Bushmanov
  Mar 8 at 6:35
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Ah yes, you are right, I also add lemmatizer in cab_tokenizer so it could be the reason. The ngram_range=(1,2) means it analyse up to bigram, which is identical with collocations=True on WordCloud.
  
  – Darren Christopher
  Mar 8 at 7:00
  

  
  
  
  

add a comment | 

I would like to count the term frequency across the corpus. To do that, there are two ways, which was using CountVectorizer and sum in axis=0 as below.

count_vec = CountVectorizer(tokenizer=cab_tokenizer, ngram_range=(1,2), stop_words=stopwords)
cv_X = count_vec.fit_transform(string_list)

Another way is using WordCloud.process_text() (see doc here) which will result in term-frequency dict. I used stopword from previously TfIdfVectorizer using tfidf_vec.get_stop_words().

text_freq = WordCloud(stopwords=stopwords, collocations=True).process_text(text)

The fact that I am using stopwords from the TfIdfVectorizer, I am expecting this to behave the same, however, the features/terms I am getting is different (length of the dict is less than TfIdfVectorizer.get_feature_names().

So, I am wondering, what is the different of using one over another? Is one more accurate than the other?

python python-3.x scikit-learn word-cloud countvectorizer

share|improve this question

asked Mar 8 at 4:28

Darren ChristopherDarren Christopher

427315

count_vec = CountVectorizer(tokenizer=cab_tokenizer, ngram_range=(1,2), stop_words=stopwords)
cv_X = count_vec.fit_transform(string_list)

text_freq = WordCloud(stopwords=stopwords, collocations=True).process_text(text)

share|improve this question

asked Mar 8 at 4:28

Darren ChristopherDarren Christopher

427315

share|improve this question

asked Mar 8 at 4:28

Darren ChristopherDarren Christopher

427315

asked Mar 8 at 4:28

Darren ChristopherDarren Christopher

427315

asked Mar 8 at 4:28

Darren ChristopherDarren Christopher

427315