5 Best Ways to Embed Text Data into Dimensional Vectors Using Python

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πŸ’‘ Problem Formulation: In natural language processing (NLP), representing text data as numerical vectors is crucial for machine learning algorithms to process and understand language. Given a dataset comprising textual content, for example, a collection of tweets, the desired output is a transformed dataset where each tweet is represented as a vector in a high-dimensional space for further analysis or modeling.

Method 1: Bag of Words (BoW)

Bag of Words is a fundamental text vectorization technique. It involves creating a vocabulary of all the unique words in the text corpus and representing each document as a count vector of the frequency of each word. The major limitation is that it ignores word order and context.

Here’s an example:

from sklearn.feature_extraction.text import CountVectorizer

# Sample text data
corpus = ['Text mining is fun.', 'Text analysis is powerful.']

# Create a CountVectorizer instance
vectorizer = CountVectorizer()

# Transform the corpus into a bag of words matrix
X = vectorizer.fit_transform(corpus)



[[0 0 1 1 1 0]
 [0 1 0 1 0 1]]
['analysis', 'fun', 'is', 'mining', 'powerful', 'text']

This code snippet uses the CountVectorizer class from scikit-learn to transform the sample texts into a bag of words matrix. Each row corresponds to a document in corpus and each column represents a unique word from the text. The numeric values are the frequencies of the corresponding word in each document.

Method 2: TF-IDF

Term Frequency-Inverse Document Frequency (TF-IDF) builds on the BoW concept but also accounts for the relative importance of a word based on how frequently it appears across documents. Words that are common across all documents are penalized, which helps in highlighting significant words unique to documents.

Here’s an example:

from sklearn.feature_extraction.text import TfidfVectorizer

# Sample text data
corpus = ['Text mining has unique challenges.', 'Text analysis unlocks potential.']

# Create a TfidfVectorizer instance
vectorizer = TfidfVectorizer()

# Transform the corpus into a TF-IDF matrix
X = vectorizer.fit_transform(corpus)



[[0.         0.         0.57615236 0.57615236 0.40993715 0.40993715]
 [0.57615236 0.57615236 0.         0.         0.40993715 0.40993715]]
['analysis', 'challenges', 'has', 'mining', 'potential', 'text', 'unlocks', 'unique']

The TfidfVectorizer class from scikit-learn is used here to represent the sample texts as a TF-IDF matrix. Each row signifies a document and each column represents the TF-IDF score of a term in the document. High scores indicate important and unique words within the document in the context of the entire corpus.

Method 3: Word Embeddings

Word embeddings provide a dense representation of words in a low-dimensional vector space. This approach captures semantic meanings and relationships between words. Pre-trained models like Word2Vec or GloVe are commonly used, mapping words to vectors such that semantically similar words are closer in the vector space.

Here’s an example:

from gensim.models import KeyedVectors

# Load pre-trained Word2Vec model
model = KeyedVectors.load_word2vec_format('word2vec.6B.50d.txt', binary=False)

# Sample words
words = ['text', 'mining', 'analysis', 'fun']

# Get vectors for each word
vectors = [model[word] for word in words]



[array([...]), array([...]), array([...]), array([...])]

In this example, the gensim.models.KeyedVectors class is utilized to load a pre-trained Word2Vec model. Vectors for the sample words are retrieved using the model, with each array representing a word’s embedding in the model’s vector space. These vectors capture the semantic meaning of each word.

Method 4: One-Hot Encoding

One-hot encoding is a simple vectorization technique where each word in the vocabulary is represented by a vector with all zeros and a single one at the index corresponding to the word in the vocabulary. This method results in a sparse matrix where each word is independently represented without any semantic meaning.

Here’s an example:

from keras.preprocessing.text import Tokenizer

# Sample text data
corpus = ['Data Science is the best.', 'AI is the future.']

# Initialize the Tokenizer
tokenizer = Tokenizer()

# Fit the tokenizer on the corpus

# Transform each text into a sequence of integers
sequences = tokenizer.texts_to_sequences(corpus)

# One-hot encode the sequences
one_hot_results = tokenizer.sequences_to_matrix(sequences, mode='binary')



[[0. 1. 1. 1. 1. 0. 0. 1. 1.]
 [0. 1. 1. 0. 0. 1. 1. 1. 0.]]

Here, Keras’s Tokenizer class is used to one-hot encode the corpus. The corpus is initially tokenized, with each token (word) assigned a unique integer index. The texts_to_sequences method transforms each sentence into a sequence of these indices, which are then converted into binary class matrix representations using the sequences_to_matrix method with the ‘binary’ mode.

Bonus One-Liner Method 5: Hashing Vectorization

Hashing vectorization is an efficient approach which uses a hash function to convert terms to indices in a fixed-size vector, rather than holding the entire vocabulary in memory. This approach can handle large datasets well but has the downside of potential hash collisions.

Here’s an example:

from sklearn.feature_extraction.text import HashingVectorizer

# Sample text data
corpus = ['Hello Python', 'Python is great', 'NLP is awesome']

# Apply hashing vectorization in a one-liner
hashed_features = HashingVectorizer(n_features=8).transform(corpus).toarray()



[[ 0.          0.          0.70710678  0.         -0.70710678  0.          0.          0.        ]
 [ 0.          0.          0.70710678  0.          0.          0.          0.         -0.70710678]
 [ 0.          0.          0.70710678  0.          0.          0.70710678  0.          0.        ]]

By employing HashingVectorizer from scikit-learn with a specified n_features parameter, the example quickly transforms the sample text corpus into a hash feature matrix. The result is an array where each sample is mapped to a fixed-size vector, using hashing to index the terms, a method that’s memory-efficient and scalable.


  • Method 1: Bag of Words (BoW). Simple and intuitive. Good for small vocabulary sizes. Ignores syntax and word order.
  • Method 2: TF-IDF. Weighs term importance. Handles common words effectively. Still loses order and semantic meaning.
  • Method 3: Word Embeddings. Captures semantic meaning. Good for deep learning models. Requires a pre-trained model and significant memory.
  • Method 4: One-Hot Encoding. Very simple to apply. Creates a large, sparse matrix. Fails to capture any semantic meaning.
  • Bonus Method 5: Hashing Vectorization. Memory-efficient. Scales well with dataset size. Potential for hash collisions and irreversibility.