Text classification pipeline in ML Python

Introduction

We use a text classification pipeline to teach a computer how to sort text into groups. This helps us quickly understand or organize lots of text.

Sorting emails into spam or not spam

Classifying customer reviews as positive or negative

Organizing news articles by topic like sports or politics

Filtering social media posts by sentiment

Detecting language of a text automatically

Syntax

ML Python

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

pipeline = Pipeline([
    ('vectorizer', TfidfVectorizer()),
    ('classifier', LogisticRegression())
])

The pipeline chains steps: first it changes text into numbers, then it learns to classify.

Each step has a name and a model or transformer.

Examples

This example removes common English words and allows more training steps for better learning.

ML Python

pipeline = Pipeline([
    ('vectorizer', TfidfVectorizer(stop_words='english')),
    ('classifier', LogisticRegression(max_iter=1000))
])

This uses a simple word count and a Naive Bayes classifier, good for quick text sorting.

ML Python

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB

pipeline = Pipeline([
    ('vectorizer', CountVectorizer()),
    ('classifier', MultinomialNB())
])

Sample Model

This program trains a text classifier to tell positive from negative reviews. It shows predictions and accuracy on test data.

ML Python

from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import TfidfVectorizer
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
texts = [
    'I love this product',
    'This is the worst thing ever',
    'Absolutely fantastic experience',
    'I hate it',
    'Not bad, could be better',
    'I am very happy with this',
    'Terrible, do not buy',
    'Best purchase I made',
    'Awful, waste of money',
    'Pretty good overall'
]
labels = [1, 0, 1, 0, 0, 1, 0, 1, 0, 1]  # 1=positive, 0=negative

# Split data
X_train, X_test, y_train, y_test = train_test_split(texts, labels, test_size=0.3, random_state=42)

# Create pipeline
pipeline = Pipeline([
    ('vectorizer', TfidfVectorizer()),
    ('classifier', LogisticRegression(max_iter=1000))
])

# Train model
pipeline.fit(X_train, y_train)

# Predict on test data
predictions = pipeline.predict(X_test)

# Calculate accuracy
accuracy = accuracy_score(y_test, predictions)

print(f'Predictions: {predictions}')
print(f'Accuracy: {accuracy:.2f}')

Important Notes

Always split your data into training and testing sets to check how well your model works on new text.

TfidfVectorizer turns text into numbers by counting words and how important they are.

LogisticRegression is a simple but effective model for two-class text classification.

Summary

A text classification pipeline turns raw text into numbers and then learns to sort it.

It helps automate sorting tasks like spam detection or sentiment analysis.

Using pipelines keeps your code clean and easy to manage.

Practice

1. What is the main purpose of a text classification pipeline in machine learning?

easy

A. To translate text from one language to another

B. To generate new text from existing data

C. To store text data in a database

D. To convert raw text into numbers and classify it

Solution

1. Step 1: Understand the role of a pipeline

   A pipeline processes raw input data step-by-step to prepare it for a model.

2. Step 2: Identify the pipeline's function in text classification

   It converts text into numbers and then classifies the text into categories.

3. Final Answer:

   To convert raw text into numbers and classify it -> Option D

4. Quick Check:

   Text classification pipeline = convert + classify [OK]

Hint: Remember: pipeline = text to numbers + classify [OK]

Common Mistakes:

• Confusing classification with text generation
• Thinking pipeline stores data only
• Mixing translation with classification

2. Which of the following is the correct way to create a simple text classification pipeline using scikit-learn?

easy

A. Pipeline('vect' = CountVectorizer(), 'clf' = LogisticRegression())

B. Pipeline(('vect', CountVectorizer(), 'clf', LogisticRegression()))

C. Pipeline([('vect', CountVectorizer()), ('clf', LogisticRegression())])

D. Pipeline({'vect': CountVectorizer(), 'clf': LogisticRegression()})

Solution

1. Step 1: Recall the Pipeline syntax in scikit-learn

   Pipeline expects a list of tuples, each tuple with a name and a transformer or estimator.

2. Step 2: Check each option's syntax

   Pipeline([('vect', CountVectorizer()), ('clf', LogisticRegression())]) uses a list of tuples correctly. Options A, B, and C use wrong syntax (missing list, wrong brackets, or invalid keyword arguments).

3. Final Answer:

   Pipeline([('vect', CountVectorizer()), ('clf', LogisticRegression())]) -> Option C

4. Quick Check:

   Pipeline needs list of (name, step) tuples [OK]

Hint: Pipeline needs list of (name, step) tuples [OK]

Common Mistakes:

• Using parentheses instead of list brackets
• Passing dictionary instead of list
• Using keyword arguments inside Pipeline

3. Given the following code, what will be the output of print(pipeline.predict(['I love this movie']))?

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

pipeline = Pipeline([
    ('vect', CountVectorizer()),
    ('clf', LogisticRegression())
])

pipeline.fit(['I love this movie', 'I hate this movie'], [1, 0])
print(pipeline.predict(['I love this movie']))

medium

A. [1]

B. [0]

C. [1, 0]

D. Error: model not trained

Solution

1. Step 1: Understand the training data and labels

   The model is trained with two texts: 'I love this movie' labeled 1 (positive), and 'I hate this movie' labeled 0 (negative).

2. Step 2: Predict the label for 'I love this movie'

   Since this exact text was labeled 1 during training, the model should predict 1.

3. Final Answer:

   [1] -> Option A

4. Quick Check:

   Same text as positive label predicts 1 [OK]

Hint: Same input as positive label predicts positive [OK]

Common Mistakes:

• Assuming prediction returns multiple labels
• Forgetting to fit the model before predict
• Confusing label 0 and 1

4. Identify the error in this text classification pipeline code:

from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB

pipeline = Pipeline([
    ('tfidf', TfidfVectorizer),
    ('clf', MultinomialNB())
])

pipeline.fit(['spam message', 'ham message'], [1, 0])

medium

A. MultinomialNB should be replaced with LogisticRegression

B. TfidfVectorizer is not instantiated with parentheses

C. Labels should be strings, not integers

D. Pipeline steps should be a dictionary, not a list

Solution

1. Step 1: Check the pipeline step definitions

   The 'tfidf' step uses TfidfVectorizer without parentheses, so it's a class, not an instance.

2. Step 2: Understand the need for instantiation

   Pipeline steps require instances of transformers/estimators, so TfidfVectorizer() with parentheses is needed.

3. Final Answer:

   TfidfVectorizer is not instantiated with parentheses -> Option B

4. Quick Check:

   Instantiate transformers with () [OK]

Hint: Always add () to instantiate transformers [OK]

Common Mistakes:

• Forgetting parentheses when creating transformer
• Changing model type unnecessarily
• Using wrong data types for labels
• Using dict instead of list for pipeline steps

5. You want to build a text classification pipeline that handles both text cleaning (lowercasing and removing punctuation) and classification. Which approach correctly integrates custom text cleaning into the pipeline?

hard

A. Create a custom transformer class for cleaning, then add it before vectorizer in the pipeline

B. Add text cleaning code inside the LogisticRegression model

C. Clean text after vectorization step in the pipeline

D. Use CountVectorizer with lowercase=True and skip custom cleaning

Solution

1. Step 1: Understand pipeline order and custom steps

   Text cleaning must happen before vectorization to affect input text properly.

2. Step 2: Implement custom cleaning as a transformer

   Creating a custom transformer class allows integration into the pipeline before vectorizer.

3. Step 3: Evaluate other options

   Add text cleaning code inside the LogisticRegression model is wrong because models don't clean text. Clean text after vectorization step in the pipeline is too late to clean text. Use CountVectorizer with lowercase=True and skip custom cleaning only lowercases but does not remove punctuation.

4. Final Answer:

   Create a custom transformer class for cleaning, then add it before vectorizer in the pipeline -> Option A

5. Quick Check:

   Custom cleaning before vectorizer = Create a custom transformer class for cleaning, then add it before vectorizer in the pipeline [OK]

Hint: Clean text before vectorizing using custom transformer [OK]

Common Mistakes:

• Trying to clean text inside the model
• Cleaning text after vectorization
• Relying only on vectorizer lowercase without punctuation removal