Bag of Words
Bag of Words (BoW) is a natural language processing (NLP) technique that represents text data as an unordered collection of words, ignoring grammar and word order but keeping track of word frequency. It converts documents into numerical feature vectors by counting how many times each word appears, making it a simple and efficient method for text classification and information retrieval tasks. Despite its simplicity, it serves as a foundational model for many text-based machine learning applications.
Developers should learn Bag of Words when working on text classification, spam detection, sentiment analysis, or document similarity tasks, as it provides a straightforward way to transform textual data into a format usable by machine learning algorithms. It is particularly useful in scenarios where word frequency is a strong indicator of content, such as in topic modeling or basic language processing pipelines, though it is often combined with more advanced techniques for better performance.