Calculate TF IDF using sklearn for n grams in python

Title: Calculating TF-IDF for N-grams Using Scikit-Learn in Python
Introduction:
Term Frequency-Inverse Document Frequency (TF-IDF) is a numerical statistic that reflects the importance of a term in a collection of documents. It is widely used in natural language processing and information retrieval. In this tutorial, we'll explore how to calculate TF-IDF for N-grams using the scikit-learn library in Python.
Requirements:
Make sure you have scikit-learn installed. If not, you can install it using:
Code Example:
Explanation:
Import Libraries: Import the TfidfVectorizer class from scikit-learn.
Sample Documents: Create a list of sample documents for demonstration.
Create TF-IDF Vectorizer: Initialize the TfidfVectorizer with the desired N-gram range (in this case, unigrams and bigrams).
Fit and Transform: Use the fit_transform method to calculate the TF-IDF matrix based on the given documents.
Get Feature Names: Retrieve the feature names (N-grams) from the vectorizer.
Display TF-IDF Matrix: Display the TF-IDF matrix and feature names.
Optional: Convert to DataFrame: Convert the TF-IDF matrix to a Pandas DataFrame for a clearer and more organized view.
Conclusion:
Calculating TF-IDF for N-grams using scikit-learn is straightforward with the TfidfVectorizer. Adjust the ngram_range parameter to control the size of N-grams based on your specific use case. This approach helps in extracting meaningful features from a collection of text documents, which can be valuable in various natural language processing applications.
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