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hi in this video we are going to build a topic model using non-negative matrix factorization or nmf as it is abbreviated in my previous video i built a topic model using lda and in case if you are not seen the video you can click the link on the top and watch it both lda and nmf nmf can be used for topic modeling uh there are some differences which i will talk about it as we go into the details of it uh but uh lda as such is more consistent when it comes to topic model and nmf nmfr has its own application when it comes to topic model when the topics are not very coherent right so letamp;#39;s get started what iamp;#39;m doing is in the in this case iamp;#39;m importing pandas to read the file i have my scikit-learn feature extraction uh to perform the dfidf vectorizer iamp;#39;m not going to do a count vectorizer over here iamp;#39;m just going to use a tf idea factorizer but when we did lda we used like count vectorizer uh the reason is basically lda is more like an uh statistica
