Feature Extraction from Text

Feature extraction from text is central for enhancing the performance of text classification models by identifying meaningful patterns and attributes within textual data. Techniques such as n-grams, part-of-speech (POS) tagging, and named entity recognition (NER) provide structured insights into textual content, significantly improving model accuracy and interpretability. This recipe will teach you how to extract meaningful elements (or features) from a given corpus of text.

Getting ready

We'll load the essential libraries and prepare the dataset for feature extraction. Here we will use the Brown Corpus also built-in to the NLTK library. It contains 500 sources categories by genre.

1. Load the libraries:

   import numpy as np
   from sklearn.feature_extraction.text import CountVectorizer
   from sklearn.model_selection import train_test_split
   import nltk
   from nltk.corpus import brown
   from nltk.util import ngrams as nltk_ngrams
   import matplotlib.pyplot as plt

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