How to Extract Entities Using Google NLP Tool

The Google Natural Language API (NLP Tool) is a machine-driven parser that extracts Entities, Sentiment, Salience Scores, and Topical Categories from text. It approximates how Google itself may parse and semantically classify your content during crawl, indexation, and ranking.

If you want your content to match Google’s interpretation layer, this is one of the closest tools to simulate it.

Key Concepts Introduced

• Entity Recognition: Detects proper nouns, terms, and concepts from body text.
• Entity Types: Person, Place, Organization, Consumer Good, Event, etc.
• Salience Score: Measures entity relevance to the central topic.
• Sentiment: Evaluates tone attached to each entity mention.
• Category Detection: Assigns a taxonomy label (e.g., /Home & Garden/Plumbing)

Step-by-Step: How to Use Google NLP Tool for Entity Mining

1. Access the Tool

Search: Google NLP Free Checker → Use the demo tool provided by Google Cloud NLP

2. Input Source Text

• Copy text from:
  • Wikipedia Articles
  • Competitor Pages
  • Internal Pages
• Paste it into the tool

Ideal for mining entity-rich, authoritative sources (e.g., “Plumbing” Wikipedia page)

3. Run Analysis

• Click “Analyze”
• Wait for the tool to return:
  • Entities Table
  • Salience Scores
  • Sentiment Analysis
  • Content Categories (e.g., “Building Materials > Plumbing”)

4. Export Entity List

• Copy-paste the entity table into Google Sheets
• Clean the list:
  • Keep: Relevant noun entities like “Wrench”, “Pipe”, “Water Heater”
  • Discard: Noise words like “article”, “discussion”, “source”, “illustration”

How This Helps Semantic SEO

Benefit	Description
Entity Coverage	Ensures you’re addressing core and peripheral concepts
Topical Alignment	Helps your content get classified under the correct category in Google’s knowledge layer
Sentiment Adjustment	Useful for product reviews, testimonials, service articles
Salience Analysis	Tells you whether your article is “about” the entity or merely “mentions” it

ALSO READ …

• How Google detects entities using NLP
• How to extract entities from Textrazor (Free Tool)
• How to help Google find entities on your content
• What is structured data
• What is entity and its types

Practical Implementation Framework

E-A-V Model Recap:

• Entity = Plumbing
• Attribute = Tool Used
• Value = Pipe Wrench

Example Output from Google NLP Tool

Entity	Type	Salience	Sentiment
Plumbing	Consumer Good	0.89	Neutral
Pipe Fitting	Thing	0.72	Neutral
Bathroom	Location	0.41	Slightly Positive
Water Heater	Consumer Good	0.33	Neutral

Optimization Tactics Post-Extraction

1. Cluster the Entities by type: tools, services, materials, room types
2. Develop Subsections or H2s for each semantic group
3. Sprinkle Attributes & Values to form E-A-V triples
4. Run Final Draft Through NLP Again:
   • Check if target entities show high salience
   • Confirm category alignment (e.g., “/Home Improvement/Plumbing”)

Advanced Use Cases

Use Case	Outcome
NLP + Wikipedia Entities	Create first and second-degree topical maps
NLP + Sentiment Data	Improve product pages or service copy
NLP + Salience Score	Determine anchor entity for content
NLP + Category Detection	Validate entity-to-topic relevance

Suggested Sheet Columns for Entity Curation

Entity Name	Type	Salience	Sentiment	Keep? (Y/N)	Category
Wrench	Thing	0.76	Neutral	Y	Tool
Emergency Repair	Event	0.62	Positive	Y	Service
Vector	Design Term	0.45	Neutral	N	Graphic Concept
Mario	Brand	0.33	Neutral	N	Entertainment

Semantic SEO Philosophy Reminder:

Google doesn’t just parse strings of words.
It builds knowledge graphs of relationships.

And entities are the building blocks.

Coming in Part 19: How to Extract Entities from TextRazor (Free Tool) – Part 19

Disclaimer: This [embedded] video is recorded in Bengali Language. You can watch with auto-generated English Subtitle (CC) by YouTube. It may have some errors in words and spelling. We are not accountable for it.