One of the questions that often comes up is what’s the difference between fastText and Word2Vec? Aren’t they both the same? Yes and no. They are conceptually the same, but there is a minor difference—fastText operates at a character level but Word2Vec […]
Previously, we learned how to use CountVectorizer for text processing. In place of CountVectorizer, you also have the option of using HashingVectorizer. In this tutorial, we will learn how HashingVectorizer differs from CountVectorizer and when to use which. CountVectorizer vs.
Scikit-learn’s CountVectorizer is used to transform a corpora of text to a vector of term / token counts. It also provides the capability to preprocess your text data prior to generating the vector representation making it a highly flexible feature
Text classification is the automatic process of predicting one or more categories given a piece of text. For example, predicting if an email is legit or spammy. Thanks to Gmail’s spam classifier, I don’t see or hear from spammy emails!
Training a Word2Vec model with phrases is very similar to training a Word2Vec model with single words. The difference: you would need to add a layer of intelligence in processing your text data to pre-discover phrases. In this tutorial, you
