What Vectorization Meaning, Applications & Example
The process of converting text or data into a numerical format.
What is Vectorization?
Vectorization is the process of converting data into a numerical format that can be processed by machine learning algorithms. In the context of text data, it refers to converting words, phrases, or documents into vectors (numerical representations). These vectors are often used in natural language processing (NLP) tasks, enabling models to perform operations on textual data effectively.
Methods of Vectorization
- One-Hot Encoding : Represents each word as a vector with a 1 for the word’s index in the vocabulary and 0s for all other indices.
- TF-IDF (Term Frequency-Inverse Document Frequency): Weighs words based on their frequency in a document and how unique they are across a set of documents, often used in text classification tasks.
- Word2Vec : A neural network model that learns word embeddings by predicting context words in a given window, capturing semantic relationships between words.
- GloVe (Global Vectors for Word Representation): A model that factors in global word co-occurrence statistics to create word vectors, capturing semantic meaning more effectively than simple frequency-based methods.
Applications of Vectorization
- Text Classification: Transforming text into vectors so it can be used as input for classification models (e.g., spam detection, sentiment analysis ).
- Recommendation Systems: Converting items (e.g., products, movies) and user preferences into vectors to compute similarity scores.
- Machine Translation: Using vectorized representations of words or sentences for translating between languages.
Example of Vectorization
In sentiment analysis, a text classifier might first convert product reviews into vectors using TF-IDF or Word2Vec. The model then processes these vectors to classify the sentiment of the review as positive or negative, helping businesses monitor customer feedback at scale.