Text Classification in Natural Language Processing
Text classification is a core task in Natural Language Processing where text
documents are automatically assigned to predefined categories or labels.
It is one of the most widely used NLP applications in industry.
From spam detection to news categorization and intent detection in chatbots,
text classification helps machines understand and organize large volumes of
text data efficiently.
⭐ What is Text Classification?
Text classification is the process of assigning one or more labels to a piece
of text based on its content. These labels can represent topics, intent,
sentiment, or categories.
📌 Types of Text Classification
- Binary Classification: Spam vs Ham
- Multi-Class Classification: News categories
- Multi-Label Classification: One text with multiple tags
📌 Common Algorithms for Text Classification
- Naive Bayes
- Logistic Regression
- Support Vector Machines (SVM)
- Deep Learning (CNN, LSTM, Transformers)
📌 Traditional ML Example (Naive Bayes)
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
texts = [
"This movie is amazing",
"I hate this product",
"The service was excellent",
"Worst experience ever"
]
labels = [1, 0, 1, 0]
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(texts)
model = MultinomialNB()
model.fit(X, labels)
📌 Deep Learning Example (LSTM)
from tensorflow import keras
from keras import layers
model = keras.Sequential([
layers.Embedding(10000, 64),
layers.LSTM(128),
layers.Dense(4, activation='softmax')
])
📌 Feature Extraction Techniques
- Bag of Words
- TF-IDF
- Word Embeddings
📌 Evaluation Metrics
- Accuracy
- Precision
- Recall
- F1-score
- Confusion Matrix
📌 Real-Life Applications
- Email spam filtering
- News article categorization
- Intent detection in chatbots
- Customer support ticket routing
📌 Project Title
Automated News and Document Classification System
📌 Project Description
In this project, you will build a text classification system that automatically
categorizes documents into predefined topics such as sports, politics,
technology, and business. This system can be extended to emails, blogs,
and customer queries.
📌 Summary
Text classification enables machines to organize and understand text at scale.
By combining effective feature extraction with machine learning or deep learning
models, highly accurate classifiers can be built for real-world applications.
This chapter prepares you for advanced NLP tasks like topic modeling and transformers.
