Choosing the right NLP tool for feature extraction: Count Vectorizer vs Tf-Idf (Part2)
Now that we’ve gotten a handle on Count Vectorizer, let’s dive into another popular method for feature extraction in NLP — Tf-Idf. Tf-Idf stands for Term Frequency-Inverse Document Frequency, and it’s a method that measures the importance of each word in a document compared to the entire corpus of text data.
The Tf-Idf process involves two calculations — term frequency and inverse document frequency. Term frequency measures the number of times a word appears in a document, and inverse document frequency measures the rarity of a word in the text data as a whole. Tf-Idf combines these two calculations to determine the importance of each word in a document.
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sample = df.text.sample(n=5 , random_state=1)
sample 
tfidf = TfidfVectorizer()
tfidf_vec = tfidf.fit_transform(sample)
tfidf_vec.shape
tfidf.get_feature_names()[:20]
tfidf.vocabulary_
tfidf_vec.toarray()
#Finally, we'll create a dataframe to better show the TF-IDF scores of each document
df_tfidf =pd.DataFrame(tfidf_vec.toarray() , columns = tfidf.get_feature_names())
df_tfidf
Use cases for Count Vectorizer and Tf-Idf
Count Vectorizer is a simple and straightforward feature extraction technique that counts the number of occurrences of each word in a text document. It treats each document as a collection of words, ignoring grammar, syntax, and the order of words. This makes Count Vectorizer a great choice for tasks such as text classification, sentiment analysis, and topic modeling.
Tf-Idf, on the other hand, stands for Term Frequency-Inverse Document Frequency. This technique not only considers the frequency of words in a document, but also their significance. It assigns a weight to each word based on its importance in a document compared to the entire corpus of documents. Tf-Idf is ideal for tasks such as text summarization, document classification, and information retrieval. Most of the text-based recommender systems uses TF-IDF.
Well, we’ve reached the end of our journey through the exciting world of feature extraction in NLP. To recap, we’ve discussed the definition of NLP, explained the concept of feature extraction, and compared the two most popular tools for feature extraction: Count Vectorizer and Tf-Idf.
In conclusion, I hope that this article has provided you with a good understanding of feature extraction in NLP, and has given you the knowledge you need to choose the right tool for your NLP task
