How can a text message become vectors?
Text messages are made of words, and computers work best with numbers. Turning text into vectors means converting a message into a list of numeric values that represent it. These vectors can then be used for search, spam detection, sentiment analysis, clustering, or feeding machine learning models.
What does “vectorizing text” mean?
A vector is simply an ordered list of numbers, like [0, 1, 3] or [0.12, -0.04, 0.88]. When you vectorize a text message, you pick a method that maps the message to numbers while keeping useful signals:
- Which words appear
- How often they appear
- Which words matter more than others
- Sometimes, what the message means in context
Below are easy examples that show several common approaches.
Example message
We’ll use this short message:
“Meet me at 5”
And sometimes a second message:
“Meet me at 6”
Even tiny changes should create slightly different vectors.
Step 1: Basic cleaning and tokenization
Most methods start by splitting a message into tokens (often words). A simple tokenization:
- Message:
"Meet me at 5" - Tokens:
["meet", "me", "at", "5"]
Lowercasing helps merge “Meet” and “meet” into the same token.
Method 1: One-hot encoding (word presence)
Create a vocabulary (a fixed list of possible tokens). Suppose your vocabulary is:
["meet", "me", "at", "5", "6"]
Now represent each message with a 0/1 vector showing whether each token appears.
- “Meet me at 5” →
[1, 1, 1, 1, 0] - “Meet me at 6” →
[1, 1, 1, 0, 1]
This is easy to read, but the vector grows as the vocabulary grows, and it treats all words as equally important.
Method 2: Bag of Words (word counts)
Instead of just presence, store counts. With the same vocabulary:
- “meet me at 5 meet” → tokens contain “meet” twice
Vector →[2, 1, 1, 1, 0]
Counts help for longer messages, but the method still ignores word order. “me meet at 5” becomes the same vector as “meet me at 5”.
Method 3: TF-IDF (discount common words)
TF-IDF gives lower weight to words that appear in many messages (“at”, “me”) and higher weight to words that help distinguish messages. Suppose across a small chat dataset, “at” appears in almost every message. TF-IDF might produce:
- “Meet me at 5” →
[0.40, 0.10, 0.02, 0.80, 0.00] - “Meet me at 6” →
[0.40, 0.10, 0.02, 0.00, 0.80]
The exact numbers depend on the dataset, but the idea is consistent: rare or specific terms often get more weight.
Method 4: Word embeddings (dense vectors for each word)
Embeddings represent each word as a dense numeric vector, such as 3–300 numbers. For a toy example with 3D vectors:
meet → [0.2, 0.1, 0.7]me → [0.0, 0.3, 0.1]at → [0.1, 0.1, 0.1]5 → [0.9, 0.0, 0.2]
To get a message vector, a simple approach is averaging word vectors:
Message vector = average of the token vectors
Result (roughly) → [0.30, 0.125, 0.275]
This creates short vectors and can group related words closer together, but averaging loses word order.
Method 5: Sentence embeddings (one vector for the whole message)
Sentence embeddings create one vector directly for the full message, often capturing more context than word averaging. A message might become a 384-dimensional vector like:
- “Meet me at 5” →
[0.01, -0.07, 0.22, ...] - “Meet me at 6” →
[0.02, -0.06, 0.20, ...]
These vectors can be compared using cosine similarity to find messages with similar meaning.
Choosing a method
- One-hot / Bag of Words: simple, transparent, works for small tasks
- TF-IDF: strong baseline for search and classification with limited data
- Embeddings: better at grouping related terms, smaller vectors
- Sentence embeddings: useful for semantic search and “meaning”-based matching
Turning texts into vectors is mainly about picking which signals matter for your task, then applying a consistent mapping so messages become comparable numeric objects.
