Text Clustering in Python for NLP: Simple Guide with Example

To do text clustering in Python for NLP, first convert text data into numerical vectors using methods like TfidfVectorizer. Then apply clustering algorithms such as KMeans to group similar texts automatically.

Syntax

Text clustering in Python typically involves these steps:

• Vectorization: Convert text into numbers using TfidfVectorizer().
• Clustering: Use KMeans(n_clusters=number) to group texts.
• Fit and predict: Call fit() on vectors and predict() to assign clusters.

python

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import KMeans

# Step 1: Convert texts to vectors
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(texts)

# Step 2: Create KMeans model
kmeans = KMeans(n_clusters=3, random_state=42)

# Step 3: Fit model and get clusters
kmeans.fit(X)
clusters = kmeans.predict(X)

Example

This example shows how to cluster a list of short texts into groups using TfidfVectorizer and KMeans. It prints each text with its assigned cluster.

python

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import KMeans

texts = [
    "I love reading books about history.",
    "The movie was fantastic and thrilling.",
    "History books provide great knowledge.",
    "That thriller movie kept me on edge.",
    "Reading novels is a relaxing hobby.",
    "Movies can be very entertaining and fun."
]

vectorizer = TfidfVectorizer(stop_words='english')
X = vectorizer.fit_transform(texts)

kmeans = KMeans(n_clusters=2, random_state=42)
kmeans.fit(X)
clusters = kmeans.labels_

for text, cluster in zip(texts, clusters):
    print(f"Cluster {cluster}: {text}")

Output

Cluster 1: I love reading books about history.
Cluster 0: The movie was fantastic and thrilling.
Cluster 1: History books provide great knowledge.
Cluster 0: That thriller movie kept me on edge.
Cluster 1: Reading novels is a relaxing hobby.
Cluster 0: Movies can be very entertaining and fun.

Common Pitfalls

Common mistakes when doing text clustering include:

• Not removing stop words, which adds noise to vectors.
• Choosing too many or too few clusters without testing.
• Using raw text instead of vectorized data for clustering.
• Ignoring random state, which makes results non-reproducible.

Always preprocess text and experiment with cluster numbers.

python

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import KMeans

texts = ["Cats are cute.", "Dogs are loyal.", "I love my cat."]

# Wrong: Using raw texts directly
# kmeans = KMeans(n_clusters=2)
# kmeans.fit(texts)  # This will raise an error

# Right: Vectorize texts first
vectorizer = TfidfVectorizer(stop_words='english')
X = vectorizer.fit_transform(texts)
kmeans = KMeans(n_clusters=2, random_state=0)
kmeans.fit(X)
print(kmeans.labels_)

Output

[1 0 1]

Quick Reference

Tips for effective text clustering:

• Use TfidfVectorizer with stop_words='english' to clean text.
• Pick n_clusters based on domain knowledge or use methods like the elbow method.
• Set random_state for reproducible results.
• Evaluate clusters qualitatively by reading sample texts per cluster.

Key Takeaways

Convert text to numerical vectors using TfidfVectorizer before clustering.

Use KMeans with a chosen number of clusters to group similar texts.

Remove stop words to improve clustering quality.

Set random_state in KMeans for consistent results.

Test different cluster counts and evaluate clusters manually.