Bag of Words is a straightforward method used in text processing where the text is represented as an unordered collection of words, disregarding grammar and word order. Each unique word corresponds to a feature, and text is transformed into a vector. The frequency of each word is usually counted, creating a frequency vector. This model is fundamental for tasks such as sentiment analysis and is often compared with more complex models like TF-IDF and word embeddings.