Implementing Text Classification Models

Implementing text classification models allows us to categorize textual data effectively, such as sentiment analysis or topic classification. Using scikit-learn, we can employ popular algorithms including Naive Bayes, Support Vector Machines (SVM), and logistic regression to accurately predict categories based on textual input. Now, let’s look at a recipe for applying classification techniques to text-based data sets.

Getting ready

We'll begin by preparing our environment and data for classification modeling. We will use the Brown Corpus again.

1. Load the libraries:

   import pandas as pd
   from sklearn.feature_extraction.text import TfidfVectorizer
   from sklearn.model_selection import train_test_split
   from sklearn.naive_bayes import MultinomialNB
   from sklearn.svm import SVC
   from sklearn.linear_model import LogisticRegression
   from sklearn.metrics import classification_report
   import nltk
   from nltk.corpus import brown
   import matplotlib.pyplot...