Logistic regression for text in NLP

Introduction

Logistic regression helps us decide if a piece of text belongs to one group or another, like sorting emails into spam or not spam.

You want to tell if a movie review is positive or negative.

You need to classify emails as spam or not spam.

You want to detect if a tweet is about a certain topic or not.

You want to quickly sort customer feedback into categories.

You want a simple model to understand text classification.

Syntax

NLP

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

# Convert text to numbers
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(texts)

# Create and train model
model = LogisticRegression()
model.fit(X, labels)

# Predict new text
new_X = vectorizer.transform(new_texts)
predictions = model.predict(new_X)

CountVectorizer turns words into numbers the model can understand.

LogisticRegression learns to separate text into classes based on these numbers.

Examples

This example trains on two sentences and predicts the sentiment of a new sentence.

NLP

texts = ['I love this movie', 'This movie is bad']
labels = [1, 0]

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

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

new_texts = ['I hate this movie']
new_X = vectorizer.transform(new_texts)
prediction = model.predict(new_X)
print(prediction)

This example classifies messages as spam (1) or not spam (0).

NLP

texts = ['spam offer', 'hello friend', 'win money now', "let's meet"]
labels = [1, 0, 1, 0]

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

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

new_texts = ['win a prize']
new_X = vectorizer.transform(new_texts)
prediction = model.predict(new_X)
print(prediction)

Sample Model

This program trains a logistic regression model to classify text as positive or negative. It splits data, trains, tests, and shows accuracy and predictions.

NLP

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# Sample text data and labels (1=positive, 0=negative)
texts = [
    'I love this product',
    'This is the worst thing ever',
    'Absolutely fantastic experience',
    'I hate it',
    'Not good at all',
    'Best purchase I made',
    'Terrible quality',
    'I am very happy',
    'Do not buy this',
    'Highly recommend it'
]
labels = [1, 0, 1, 0, 0, 1, 0, 1, 0, 1]

# Split data into training and testing sets
X_train_texts, X_test_texts, y_train, y_test = train_test_split(texts, labels, test_size=0.3, random_state=42)

# Convert text to numbers
vectorizer = CountVectorizer()
X_train = vectorizer.fit_transform(X_train_texts)
X_test = vectorizer.transform(X_test_texts)

# Create and train logistic regression model
model = LogisticRegression()
model.fit(X_train, y_train)

# Predict on test data
y_pred = model.predict(X_test)

# Calculate accuracy
accuracy = accuracy_score(y_test, y_pred)

print(f"Test accuracy: {accuracy:.2f}")
print(f"Test texts: {X_test_texts}")
print(f"Predictions: {y_pred}")

Important Notes

Logistic regression works best with simple, clear text data.

Text must be converted to numbers before training.

More data usually means better results.

Summary

Logistic regression can classify text into categories like positive or negative.

Text is first changed into numbers using tools like CountVectorizer.

Model learns from examples and then predicts new text labels.

Practice

1. What is the main purpose of logistic regression when applied to text data?

easy

A. To count the number of words in a text

B. To generate new text sentences

C. To classify text into categories like positive or negative

D. To translate text from one language to another

Solution

1. Step 1: Understand logistic regression's role in text

   Logistic regression is a method used to classify data into categories based on input features.

2. Step 2: Apply to text classification

   When applied to text, logistic regression predicts categories like positive or negative sentiment.

3. Final Answer:

   To classify text into categories like positive or negative -> Option C

4. Quick Check:

   Logistic regression classifies text [OK]

Hint: Logistic regression predicts categories, not generates text [OK]

Common Mistakes:

• Confusing classification with text generation
• Thinking logistic regression translates languages
• Assuming it only counts words

2. Which Python library is commonly used to convert text into numbers before applying logistic regression?

easy

A. CountVectorizer

B. matplotlib

C. pandas

D. seaborn

Solution

1. Step 1: Identify text to number conversion tools

   CountVectorizer is a tool that converts text into a matrix of token counts, suitable for models.

2. Step 2: Match with logistic regression preprocessing

   Before logistic regression, text must be numeric; CountVectorizer is commonly used for this.

3. Final Answer:

   CountVectorizer -> Option A

4. Quick Check:

   Text to numbers = CountVectorizer [OK]

Hint: CountVectorizer turns words into numbers for models [OK]

Common Mistakes:

• Choosing plotting libraries like matplotlib
• Confusing data frame libraries like pandas
• Selecting visualization tools like seaborn

3. What will be the output of this code snippet?

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

texts = ['good movie', 'bad movie']
labels = [1, 0]

vectorizer = CountVectorizer()
X = vectorizer.fit_transform(texts)
model = LogisticRegression()
model.fit(X, labels)
pred = model.predict(vectorizer.transform(['good movie']))
print(pred)

medium

A. [0]

B. [1]

C. [1, 0]

D. Error: model not trained

Solution

1. Step 1: Understand training data and labels

   Texts 'good movie' labeled 1 (positive), 'bad movie' labeled 0 (negative).

2. Step 2: Predict on 'good movie'

   Model trained on these examples predicts label for 'good movie' as 1.

3. Final Answer:

   [1] -> Option B

4. Quick Check:

   Prediction for 'good movie' = 1 [OK]

Hint: Model predicts label matching training example [OK]

Common Mistakes:

• Assuming prediction returns multiple labels
• Thinking model is untrained causing error
• Confusing label 0 and 1

4. Identify the error in this code snippet for logistic regression on text:

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

texts = ['happy', 'sad']
labels = [1, 0]

vectorizer = CountVectorizer()
X = vectorizer.fit_transform(texts)
model = LogisticRegression()
model.fit(texts, labels)

medium

A. model.fit should use numeric features, not raw texts

B. CountVectorizer is not imported

C. fit_transform should be called on labels

D. Labels should be strings, not integers

Solution

1. Step 1: Check input to model.fit

   Model expects numeric features, but code passes raw text strings.

2. Step 2: Correct usage of vectorized data

   Must pass X (vectorized text) to model.fit, not original texts.

3. Final Answer:

   model.fit should use numeric features, not raw texts -> Option A

4. Quick Check:

   Model needs numbers, not raw text [OK]

Hint: Pass vectorized text, not raw strings, to model.fit [OK]

Common Mistakes:

• Passing raw text instead of vectorized data
• Confusing labels data type requirements
• Ignoring import statements

5. You trained a logistic regression model on text data using CountVectorizer. When testing on new sentences, the model predicts only one class for all inputs. What is the best way to improve the model's performance?

hard

A. Change logistic regression to linear regression

B. Remove CountVectorizer and use raw text directly

C. Use fewer training examples to avoid overfitting

D. Increase the number of training examples and use n-grams in CountVectorizer

Solution

1. Step 1: Understand cause of single-class prediction

   Model may be underfitting due to limited data or simple features.

2. Step 2: Improve feature richness and data size

   Adding more training examples and using n-grams captures more context, improving model learning.

3. Final Answer:

   Increase the number of training examples and use n-grams in CountVectorizer -> Option D

4. Quick Check:

   More data + better features = better model [OK]

Hint: More data and richer features improve classification [OK]

Common Mistakes:

• Removing vectorizer loses numeric input
• Reducing data worsens model
• Confusing regression types