Python for NLP and Semantic SEO: Practical Guide with Code Examples [2026]
Python is the most effective tool for implementing NLP-driven semantic SEO because it provides libraries (spaCy, NLTK, Transformers, sentence-transformers) that automate entity extraction, semantic clustering, content gap analysis, and internal linking at scale. This guide shows how to apply each library to real semantic SEO tasks — from entity recognition to topical map generation — with working code examples.
Why Python Is Essential for Semantic SEO in 2026
Manual semantic SEO analysis is limited by human bandwidth. Python automates the processes that make semantic SEO scalable: analyzing thousands of URLs for entity coverage, clustering queries by semantic similarity, identifying internal linking gaps, and extracting structured data from competitor pages.
| Semantic SEO Task | Manual Approach | Python + NLP Approach | Time Saved |
|---|---|---|---|
| Entity extraction from 100 URLs | 5–10 hours | 15–30 minutes | ~95% |
| Query clustering (1,000 keywords) | 8–16 hours | 20–45 minutes | ~95% |
| Internal link gap analysis | 4–8 hours | 10–20 minutes | ~95% |
| Topical map generation | 2–4 hours | 30–60 minutes | ~75% |
| Content similarity analysis | Manual reading | Automated cosine similarity | ~90% |
Python NLP Libraries for Semantic SEO: Which to Use When
| Library | Best For | Skill Level | Install |
|---|---|---|---|
| spaCy | Entity recognition (NER), POS tagging, dependency parsing | Beginner–Intermediate | pip install spacy |
| NLTK | Tokenization, stemming, stopword removal, basic text analysis | Beginner | pip install nltk |
| sentence-transformers | Semantic similarity, query clustering, content deduplication | Intermediate | pip install sentence-transformers |
| Transformers (HuggingFace) | Advanced NER, text classification, semantic analysis | Advanced | pip install transformers |
| scikit-learn | TF-IDF, k-means clustering, cosine similarity | Intermediate | pip install scikit-learn |
| BeautifulSoup + requests | Web scraping, content extraction, competitor analysis | Beginner | pip install beautifulsoup4 requests |
Use Case 1: Entity Extraction with spaCy
Entity extraction identifies all named entities (organizations, people, concepts, locations) in a body of text — the first step in understanding what a page is semantically about.
import spacy
# Load English model
nlp = spacy.load("en_core_web_lg")
def extract_entities(text):
doc = nlp(text)
entities = []
for ent in doc.ents:
entities.append({
"text": ent.text,
"label": ent.label_,
"description": spacy.explain(ent.label_)
})
return entities
# Example: analyze a page's content
page_content = """Semantic SEO uses entity optimization and topical authority
to help Google understand content meaning. Koray Tuğberk Gübür developed
the framework at Holistic SEO & Digital."""
entities = extract_entities(page_content)
for e in entities:
print(f"{e['text']} → {e['label']} ({e['description']})")
SEO application: Run this on your top 10 competitor pages to identify which entities they consistently mention — then ensure your content covers those entities explicitly.
Use Case 2: Semantic Query Clustering
Semantic query clustering groups keywords by meaning rather than exact match — identifying which queries share the same user intent and should be targeted by the same page.
from sentence_transformers import SentenceTransformer
from sklearn.cluster import KMeans
import numpy as np
# Load semantic model
model = SentenceTransformer('all-MiniLM-L6-v2')
# Your keyword list from GSC or keyword research
queries = [
"what is semantic seo",
"semantic seo definition",
"how does semantic seo work",
"semantic seo vs keyword seo",
"topical authority seo",
"how to build topical authority",
"entity seo optimization",
"entity based seo strategy"
]
# Generate embeddings
embeddings = model.encode(queries)
# Cluster into groups (adjust n_clusters based on your needs)
n_clusters = 3
kmeans = KMeans(n_clusters=n_clusters, random_state=42)
clusters = kmeans.fit_predict(embeddings)
# Print clusters
for cluster_id in range(n_clusters):
print(f"\nCluster {cluster_id}:")
for i, query in enumerate(queries):
if clusters[i] == cluster_id:
print(f" - {query}")
SEO application: Use this to consolidate keyword lists from GSC into actionable content clusters. Each cluster = one page or one cluster of pages. Stop creating separate pages for semantically identical queries.
Use Case 3: Content Similarity Analysis (Cannibalization Detection)
Cosine similarity between page embeddings identifies which pages are semantically too similar — the Python equivalent of a content cannibalization audit.
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
import pandas as pd
model = SentenceTransformer('all-MiniLM-L6-v2')
# Your page titles and meta descriptions (or full content)
pages = {
"/seo/semantic-seo-guide/": "Complete guide to semantic SEO fundamentals and implementation",
"/seo/what-is-semantic-seo/": "What is semantic SEO and why it matters for rankings",
"/seo/semantic-seo-strategy/": "How to build a semantic SEO strategy for topical authority",
"/seo/topical-authority/": "Topical authority: how to build it and why it matters",
}
urls = list(pages.keys())
texts = list(pages.values())
# Generate embeddings and calculate similarity
embeddings = model.encode(texts)
similarity_matrix = cosine_similarity(embeddings)
# Flag high similarity pairs (>0.85 = potential cannibalization)
print("High similarity pairs (potential cannibalization):")
for i in range(len(urls)):
for j in range(i+1, len(urls)):
score = similarity_matrix[i][j]
if score > 0.85:
print(f" {urls[i]} ↔ {urls[j]}: {score:.3f}")
SEO application: Run monthly across all published URLs. Pairs with similarity >0.85 are cannibalization candidates — merge, redirect, or differentiate them.
Use Case 4: Internal Link Gap Analysis
This script identifies which pages in your site mention an entity or topic but don’t link to the canonical page covering that topic — revealing internal linking gaps in your semantic network.
import requests
from bs4 import BeautifulSoup
def get_page_links_and_text(url):
"""Extract all internal links and full text from a page."""
try:
response = requests.get(url, timeout=10)
soup = BeautifulSoup(response.content, 'html.parser')
# Get all internal links
links = [a['href'] for a in soup.find_all('a', href=True)
if 'pos1.ar' in a.get('href', '') or a['href'].startswith('/')]
# Get page text
text = soup.get_text(separator=' ', strip=True)
return links, text
except:
return [], ""
# Check if pages mentioning "topical authority" link to the canonical page
target_topic = "topical authority"
canonical_url = "/seo/understanding-topical-authority/"
pages_to_check = [
"https://pos1.ar/seo/koray-framework/",
"https://pos1.ar/seo/fundamentals-of-semantic-seo/",
"https://pos1.ar/seo/topical-maps-semantic-seo-framework/",
]
for page_url in pages_to_check:
links, text = get_page_links_and_text(page_url)
mentions_topic = target_topic.lower() in text.lower()
has_link = any(canonical_url in link for link in links)
if mentions_topic and not has_link:
print(f"GAP: {page_url} mentions '{target_topic}' but doesn't link to {canonical_url}")
Use Case 5: Topical Map Generation from Keyword Data
This script takes a raw list of keywords and automatically generates a topical map by clustering semantically related queries into pillar-spoke groups.
from sentence_transformers import SentenceTransformer
from sklearn.cluster import AgglomerativeClustering
import numpy as np
model = SentenceTransformer('all-MiniLM-L6-v2')
# Paste your keyword list here (from GSC, Ahrefs, Semrush)
keywords = [
"semantic seo", "what is semantic seo", "semantic seo guide",
"topical authority", "how to build topical authority", "topical authority seo",
"entity seo", "entity optimization seo", "entities in seo",
"schema markup seo", "how to add schema markup", "schema markup guide",
"internal linking seo", "semantic internal linking", "hub and spoke linking",
"koray framework", "koray seo", "koray tugberk framework"
]
embeddings = model.encode(keywords)
# Agglomerative clustering for hierarchical topic grouping
clustering = AgglomerativeClustering(
n_clusters=None,
distance_threshold=1.2,
metric='cosine',
linkage='average'
)
labels = clustering.fit_predict(embeddings)
# Output topical map
clusters = {}
for keyword, label in zip(keywords, labels):
if label not in clusters:
clusters[label] = []
clusters[label].append(keyword)
print("TOPICAL MAP:")
for cluster_id, cluster_keywords in clusters.items():
# Use the shortest keyword as pillar topic
pillar = min(cluster_keywords, key=len)
print(f"\nPillar: {pillar}")
for kw in cluster_keywords:
if kw != pillar:
print(f" └── Spoke: {kw}")
Frequently Asked Questions
Do I need advanced Python skills to implement NLP for semantic SEO?
No. The use cases above require basic Python knowledge (variables, loops, functions). spaCy and sentence-transformers have excellent documentation and simple APIs. Most semantic SEO NLP tasks can be implemented with 10–50 lines of code using these libraries.
Which Python NLP library is best for SEO entity recognition?
spaCy with the en_core_web_lg model is the best starting point for SEO entity recognition — it’s fast, accurate, and identifies 18 entity types (PERSON, ORG, GPE, PRODUCT, etc.). For more accuracy on domain-specific entities, use HuggingFace Transformers with a BERT-based NER model.
How do I use Python to find content cannibalization?
Use sentence-transformers to generate embeddings for all page titles and meta descriptions, then calculate cosine similarity between all pairs. Pages with similarity scores above 0.85 are strong cannibalization candidates. Run this analysis monthly as part of your content audit workflow.
Can Python automate topical map creation?
Yes — the clustering approach above automates 80% of topical map creation from a keyword list. The remaining 20% is editorial judgment: deciding which cluster becomes a pillar vs. spoke, and identifying missing subtopics. Combine Python clustering with manual review for best results.
How does Python NLP connect to the Koray Framework?
Python NLP automates the entity mapping and topical gap analysis steps that are foundational to the Koray Framework. Entity extraction identifies what entities a topic cluster must cover; semantic clustering groups queries into the right pillar-spoke structure; similarity analysis prevents cannibalization. Python makes the Koray Framework scalable.