Word2Vec Visualization Tool

Explore word embeddings and semantic relationships with this interactive Word2Vec simulator. Understand how words are represented as vectors in high-dimensional space.

What is Word2Vec?

Word2Vec is a neural network model that learns word embeddings - numerical representations of words as vectors in a high-dimensional space. Words with similar meanings have similar vectors, allowing mathematical operations like "king - man + woman = queen".

Word Input

king queen man woman computer science programming algorithm machine learning artificial intelligence

Royalty Example

Tech Example

Food Example

Training Word2Vec Model 0%

This simulates Word2Vec training. Real Word2Vec needs large text corpora.

Model Settings

Model Parameters

Vector Dimensions: 2D (Visualization) 3D (Visualization) 10D (Balanced) 50D (Standard) 100D (Detailed) 300D (Advanced)

Context Window:

Learning Rate:

Training Options

Use Skip-Gram (vs CBOW)

Use Negative Sampling

Normalize Vectors

Visualization

2D Projection (PCA)

3D Projection (PCA)

Show Word Labels

Color by Category

Word Embedding Visualization

Word Space Visualization

Word Similarities

Word Analogies

Generating visualization...

Nouns

Technology

Royalty

Other

Most Similar Words

"king"

• queen 0.92
• prince 0.87
• monarch 0.85
• ruler 0.82
• throne 0.78

"computer"

• machine 0.89
• technology 0.86
• software 0.84
• hardware 0.82
• device 0.79

Word Analogies (King - Man + Woman = ?)

Try Word Analogies

- +

Result:

Royalty Analogy

king - man + woman = queen

vector(king) - vector(man) + vector(woman) ≈ vector(queen)

Capital Analogy

Paris - France + Italy = Rome

vector(Paris) - vector(France) + vector(Italy) ≈ vector(Rome)

Cooking Analogy

cook - kitchen + garage = mechanic

vector(cook) - vector(kitchen) + vector(garage) ≈ vector(mechanic)

Word Embeddings (Vector Values)

Sample Word Vectors

Word	Dim 1	Dim 2	Dim 3	Dim 4	Dim 5	...	Magnitude

Understanding Word Vectors

Each word is represented as a dense vector (list of numbers). Similar words have similar vectors. The "magnitude" shows the vector length. In real Word2Vec models, vectors typically have 100-300 dimensions.

How to Add This Word2Vec Tool to Your Blogger Site

Step 1: Copy All Code

Select all the code on this page (click and drag or press Ctrl+A then Ctrl+C). The entire page is a single HTML file.

Step 2: Create New Blog Post

In your Blogger dashboard, create a new post or edit an existing one where you want to add the tool.

Step 3: Switch to HTML Mode

Click the "HTML" button in the post editor to switch from Compose to HTML mode.

Step 4: Paste & Publish

Paste the copied code (Ctrl+V) into the HTML editor, then publish or update your post.

What is Word2Vec Used For?

Word2Vec is used in natural language processing for: semantic search, text classification, sentiment analysis, machine translation, and recommendation systems. It captures semantic relationships between words, allowing computers to understand word meanings mathematically.

Word2Vec Visualization Tool | Designed for Blogger | No Coding Knowledge Required

Natural Language Processing & Word Embeddings Tool