hope someone kindly put time here,

my approach is like this: TFIDF -> LDA -> SVM

I am using LDA to extract topics. I want to do topic modelling and use the topics as features to do document classification.

I know that I have to send the feature vector to SVM, but my question is that how can I make this feature vector to send to svm? Is featureValue here is the probability assigned to each word? My question is kind of the step 3 in the below link that I dont know how to do it

svm on lda

but in this link there is no implementation, that is just explanation.

Many thanks for your time:)

  • 1
    $\begingroup$ 1), Ignore the tfidf part, you cannot use tfidf in lda, lda takes only term counts; 2), Feed the document-topic distribution vector that you get from lda, to your svm classifier. They are your feature vector. Simples. $\endgroup$
    – Blue482
    Commented Aug 4, 2017 at 0:24
  • $\begingroup$ @Blue482, May I ask you to leave the comment here datascience.stackexchange.com/questions/21947/apply-svm-on-lda I create this post just with email as guest and had not signed up, so I can not put comment as guest, I mean for commenting there is no option of comment as guest at all even with that email datascience.stackexchange.com/questions/21947/apply-svm-on-lda $\endgroup$
    – sariii
    Commented Aug 4, 2017 at 1:26

1 Answer 1


Following your code in your other thread:

# Use tf (raw term count) features for LDA.
print("Extracting tf features for LDA...")
tf_vectorizer = CountVectorizer(max_df=0.95, min_df=2,
t0 = time()
tf = tf_vectorizer.fit_transform(data_samples)
print("done in %0.3fs." % (time() - t0))

print("Topic modelling with LDA...")
lda = LatentDirichletAllocation(n_topics=n_topics, max_iter=5,

lda_x = lda.fit_transform(tf)
# so lda_x is your doc-topic distribution that you can use for feature vector to your SVM model.
# lda.components_ is your topic-word distribution.

Hope this helps!


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