To perform text classification of news articles using NLP, you can follow the following steps:

Data Collection: Collect the BBC News corpus that consists of news articles from different categories like tech, business, sport, entertainment, and politics.

Data Cleaning and Preprocessing: Clean and preprocess the data to remove unnecessary information, like special characters, punctuations, stop words, and perform stemming or lemmatization.

Feature Extraction: Extract relevant features from the preprocessed text data using techniques like Bag of Words, TF-IDF, Word Embedding, etc.

Model Training: Train a classification model using any of the algorithms like Naive Bayes, SVM, Logistic Regression, or Neural Networks.

Model Evaluation: Evaluate the performance of the trained model using metrics like accuracy, precision, recall, and F1-score.

Here's an example Python code for text classification of news articles using the BBC News corpus:

# Import required libraries
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import classification_report

# Load the dataset
bbc_news = pd.read_csv(r'C:\Users\HP\Desktop\bbc_news.csv')

# Split the data into train and test sets
X_train, X_test, y_train, y_test = train_test_split(bbc_news['Article'], bbc_news['Category'], test_size=0.2, random_state=42)
print(x_train)

# Vectorize the text data using TF-IDF
tfidf_vectorizer = TfidfVectorizer()
X_train_tfidf = tfidf_vectorizer.fit_transform(X_train)
X_test_tfidf = tfidf_vectorizer.transform(X_test)

# Train a Multinomial Naive Bayes classifier
classifier = MultinomialNB()
classifier.fit(X_train_tfidf, y_train)

# Predict the categories for test data
y_pred = classifier.predict(X_test_tfidf)

# Evaluate the performance of the classifier
print(classification_report(y_test, y_pred))


This code loads the BBC News corpus, splits it into train and test sets, vectorizes the text data using TF-IDF, trains a Multinomial Naive Bayes classifier, and evaluates its performance using classification report.