Why text classification categorizes documents in NLP

Introduction

Text classification helps organize documents by putting them into groups based on their content. This makes it easier to find, sort, and understand large amounts of text.

Sorting emails into spam or inbox folders automatically.

Tagging news articles by topic like sports, politics, or entertainment.

Filtering customer reviews as positive or negative to understand feedback.

Organizing support tickets by issue type to speed up responses.

Detecting language or sentiment in social media posts.

Syntax

NLP

model.fit(X_train, y_train)
predictions = model.predict(X_test)

fit trains the model using labeled text data.

predict assigns categories to new, unseen text.

Examples

This example trains a simple model to classify text as positive or negative, then predicts the label for a new sentence.

NLP

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

texts = ['I love cats', 'I hate rain']
labels = ['positive', 'negative']

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

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

new_text = ['I love rain']
X_new = vectorizer.transform(new_text)
prediction = model.predict(X_new)
print(prediction)

This example uses a pipeline to combine text vectorization and classification in one step.

NLP

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

texts = ['Sports are fun', 'Politics is complex']
labels = ['sports', 'politics']

model = make_pipeline(TfidfVectorizer(), LogisticRegression())
model.fit(texts, labels)

print(model.predict(['I like sports']))

Sample Model

This program trains a text classifier to categorize news articles into baseball or space topics. It shows how well the model works by printing accuracy and some predictions.

NLP

from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import accuracy_score

# Load a small subset of news articles
categories = ['rec.sport.baseball', 'sci.space']
data_train = fetch_20newsgroups(subset='train', categories=categories, remove=('headers', 'footers', 'quotes'))
data_test = fetch_20newsgroups(subset='test', categories=categories, remove=('headers', 'footers', 'quotes'))

# Convert text to numbers
vectorizer = TfidfVectorizer()
X_train = vectorizer.fit_transform(data_train.data)
X_test = vectorizer.transform(data_test.data)

# Train a simple classifier
model = MultinomialNB()
model.fit(X_train, data_train.target)

# Predict categories for test data
predictions = model.predict(X_test)

# Calculate accuracy
accuracy = accuracy_score(data_test.target, predictions)

print(f"Accuracy: {accuracy:.2f}")
print(f"First 5 predictions: {predictions[:5]}")

Important Notes

Text classification needs labeled examples to learn from.

Good text representation (like TF-IDF) helps the model understand words better.

Accuracy shows how often the model guesses the right category.

Summary

Text classification groups documents by their content automatically.

It helps organize and find information quickly.

Simple models can learn from examples and predict new text categories.