Logistic Regression for Text Classification

In the previous lesson, you learned how Naive Bayes works for text classification and why it is fast and effective.

Now we move to another extremely important algorithm: Logistic Regression.

Despite its name, Logistic Regression is not used for regression problems here. It is one of the most popular and powerful classification algorithms in NLP.

What Is Logistic Regression?

Logistic Regression is a linear classification algorithm that predicts the probability of a text belonging to a class.

Instead of directly predicting a label, it first predicts a probability between 0 and 1, then converts it into a class label.

For example:

• Probability = 0.92 → Positive
• Probability = 0.15 → Negative

Why Logistic Regression Is Used in NLP

Logistic Regression works very well for text because:

• Text data is high-dimensional
• Decision boundaries are often linear
• It scales well to large datasets

In many NLP tasks, Logistic Regression outperforms Naive Bayes when enough training data is available.

Naive Bayes vs Logistic Regression (Conceptual)

Understanding this comparison is important for interviews.

• Naive Bayes: probabilistic, fast, assumes independence
• Logistic Regression: discriminative, learns weights directly

Naive Bayes models how data is generated, while Logistic Regression models the decision boundary.

How Logistic Regression Works (Intuition)

Logistic Regression:

• Takes numeric features (word vectors)
• Assigns weights to each feature
• Computes a weighted sum
• Passes it through a sigmoid function

Words with higher importance get higher weights.

Text Classification Pipeline with Logistic Regression

The NLP pipeline remains the same:

1. Text cleaning
2. Vectorization (Bag of Words / TF-IDF)
3. Train Logistic Regression model
4. Predict labels

Only the classifier changes.

Simple Code Example: Logistic Regression for Text

This example uses:

• TF-IDF for better text representation
• Logistic Regression for classification

Where to run this code:

• Google Colab (recommended)
• Jupyter Notebook (Anaconda)

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

texts = [
    "I love this product",
    "This is amazing",
    "I hate this item",
    "This is terrible"
]

labels = [1, 1, 0, 0]  # 1 = Positive, 0 = Negative

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

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

test_text = ["This product is terrible"]
X_test = vectorizer.transform(test_text)

prediction = model.predict(X_test)
print("Prediction:", prediction)

Output Explanation:

• TF-IDF gives importance to meaningful words
• Logistic Regression learns feature weights
• The model predicts sentiment for new text

How Logistic Regression Makes Decisions

Logistic Regression:

• Does not assume word independence
• Considers all features together
• Finds a best separating boundary

This often leads to better accuracy than Naive Bayes.

Advantages of Logistic Regression

• Strong baseline for NLP tasks
• Works well with TF-IDF
• Interpretable feature weights
• Good balance of speed and accuracy

Limitations of Logistic Regression

• Assumes linear decision boundary
• Needs more data than Naive Bayes
• Cannot capture deep semantics

These limitations motivate deep learning models later.

Real-Life Applications

• Sentiment analysis
• Spam detection
• News categorization
• Customer feedback analysis

Logistic Regression is a standard industry baseline model.

Assignment / Homework

Theory:

• Explain how Logistic Regression differs from Naive Bayes
• Explain why TF-IDF is preferred over Bag of Words

Practical:

• Add more training samples
• Switch TF-IDF to CountVectorizer
• Compare predictions

Practice environment:

• Google Colab
• Jupyter Notebook

Practice Questions

Quick Quiz

Quick Recap

• Logistic Regression is a strong NLP classifier
• It learns feature weights directly
• Works best with TF-IDF
• Often outperforms Naive Bayes with enough data

In the next lesson, we will learn about Support Vector Machines (SVM) for Text Classification.