NLP Classification Models

So far, we learned how to clean text, convert it into numerical form, and represent meaning using embeddings. The next step is to use these representations to make decisions. NLP classification models take text as input and assign it to predefined categories.

This lesson explains how text classification works, where it is used in the real world, and how simple NLP classification models are built using machine learning.

Real-World Connection

Whenever an email is marked as spam or not spam, whenever a product review is labeled as positive or negative, or whenever a support ticket is routed to the correct department, NLP classification is working behind the scenes.

These systems do not understand text like humans. They learn patterns from large volumes of labeled data and use those patterns to classify new text.

What Is Text Classification?

Text classification is the task of assigning a category or label to a piece of text. The labels depend on the application.

• Spam vs Not Spam
• Positive, Negative, Neutral sentiment
• News category classification
• Intent detection in chatbots

Basic NLP Classification Pipeline

Most NLP classification systems follow the same pipeline:

• Text preprocessing
• Feature extraction (BoW, TF-IDF, or embeddings)
• Model training
• Prediction and evaluation

Simple Text Classification Example

Let us build a basic sentiment classifier using TF-IDF and Logistic Regression. This example shows the core idea behind many real-world systems.

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression

texts = [
    "I love this product",
    "This is an amazing experience",
    "I hate this service",
    "This is terrible"
]

labels = [1, 1, 0, 0]

vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(texts)

model = LogisticRegression()
model.fit(X, labels)

prediction = model.predict(vectorizer.transform(["I love this service"]))
print(prediction)
  

Understanding the Code

The text is first converted into TF-IDF vectors. These vectors are then fed into a Logistic Regression model. The model learns which words are associated with positive or negative sentiment based on training data.

The output 1 indicates a positive sentiment prediction.

Popular NLP Classification Algorithms

• Naive Bayes
• Logistic Regression
• Support Vector Machines (SVM)
• Neural Networks
• Transformer-based classifiers

Embeddings-Based Classification

Instead of TF-IDF, modern systems often use embeddings as input. This improves performance because embeddings capture semantic meaning.

Transformer-based models like BERT are commonly fine-tuned for classification tasks.

Where NLP Classification Is Used

• Email spam detection
• Sentiment analysis
• Intent detection
• Content moderation
• Customer feedback analysis

Practice Questions

Quick Quiz

Coming up next: Sequence Modeling in NLP — understanding text order and dependencies.