News Category Classifier

 News Category Classifier 

1. Introduction

The News Category Classifier is a project that uses Natural Language Processing (NLP) techniques to classify news articles into predefined categories such as sports, politics, technology, entertainment, and others. This project is widely applicable in content organization, recommendation systems, and media monitoring.

2. Prerequisites

• Python: Install Python 3.x from the official Python website.
• Required Libraries:
  - pandas: Install using pip install pandas
  - numpy: Install using pip install numpy
  - scikit-learn: Install using pip install scikit-learn
  - nltk: Install using pip install nltk
• Dataset: A labeled dataset of news articles with corresponding categories (e.g., BBC News dataset).
• Text preprocessing knowledge and feature extraction techniques.

3. Project Setup

1. Create a Project Directory:

- Name your project folder, e.g., `News_Category_Classifier`.
- Inside this folder, create the Python script file (`news_classifier.py`).

2. Install Required Libraries:

Ensure Pandas, Scikit-learn, NLTK, and other dependencies are installed using `pip`.

4. Writing the Code

Below is an example code snippet for the News Category Classifier:

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.metrics import accuracy_score, classification_report
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
import nltk

# Download NLTK data
nltk.download('punkt')
nltk.download('stopwords')

# Load dataset
data = pd.read_csv('news_data.csv')  # Dataset with 'text' and 'category' columns

# Preprocess text
stop_words = set(stopwords.words('english'))
def preprocess_text(text):
    words = word_tokenize(text.lower())
    filtered_words = [word for word in words if word.isalnum() and word not in stop_words]
    return " ".join(filtered_words)

data['text'] = data['text'].apply(preprocess_text)

# Split data
X_train, X_test, y_train, y_test = train_test_split(data['text'], data['category'], test_size=0.2, random_state=42)

# Convert text to feature vectors
vectorizer = TfidfVectorizer(max_features=5000)
X_train_vectors = vectorizer.fit_transform(X_train)
X_test_vectors = vectorizer.transform(X_test)

# Train Naive Bayes classifier
model = MultinomialNB()
model.fit(X_train_vectors, y_train)

# Evaluate model
y_pred = model.predict(X_test_vectors)
print("Accuracy:", accuracy_score(y_test, y_pred))
print("Classification Report:
", classification_report(y_test, y_pred))

# Predict category for new text
def predict_category(text):
    processed_text = preprocess_text(text)
    vector = vectorizer.transform([processed_text])
    return model.predict(vector)[0]

new_text = "The government has announced new policies for economic growth."
print(f"Category for '{new_text}':", predict_category(new_text))
   

5. Key Components

• Text Preprocessing: Cleans the input text by removing stopwords, punctuation, and other unnecessary elements.
• Feature Extraction: Converts text into numerical features using techniques like TF-IDF.
• Model Training: Trains a classification model (e.g., Naive Bayes) on the preprocessed data.
• Category Prediction: Classifies new text inputs into predefined categories.

6. Testing

1. Ensure the dataset (`news_data.csv`) is available in the project directory.

2. Run the script:

   python news_classifier.py

3. Verify the model accuracy and test with custom news articles.

7. Enhancements

• Advanced Models: Use state-of-the-art models like BERT or transformer-based architectures for better accuracy.
• Multi-Language Support: Extend the system to handle news articles in multiple languages.
• Real-Time Integration: Connect the system to live news feeds for real-time classification.

8. Troubleshooting

• Low Accuracy: Use a larger or more diverse dataset for training.
• Text Processing Errors: Check for missing or incorrect preprocessing steps.
• Library Compatibility: Verify library versions and dependencies.

9. Conclusion

The News Category Classifier effectively organizes news articles into relevant categories, providing a robust tool for content curation and analysis. This project showcases the power of NLP in automating and streamlining information management.