bagofwords

The bag-of-words model is a straightforward method to represent text data. It treats each document as a bag (multiset) of words, disregarding grammar and word order but preserving multiplicity. The idea is to convert text into a numeric representation suitable for machine learning.

To construct BoW features, a vocabulary is first built from a corpus of text. Each document is then represented as a fixed-length vector of dimension V, where V is the vocabulary size. Each component corresponds to a word in the vocabulary; the value is the word’s frequency in the document, or a binary indicator, or a weight such as term frequency (TF) or TF-IDF.

Preprocessing steps typically include tokenization, normalization (lowercasing), removal of stop words, and stemming or lemmatization. In practice, researchers may also use n-grams to capture some local word order.

Variants and extensions: Binary BoW uses 0/1 to indicate presence; Term Frequency counts occurrences; TF-IDF weights each term by its importance in the document and inverse importance in the corpus, downweighting common words.

Advantages: simple to implement, computationally efficient, scalable to large corpora, and interpretable. It provides a solid baseline for text classification, clustering, and information retrieval.

Limitations: it discards word order, syntax, and semantics; very high-dimensional and sparse; does not capture contextual meaning or polysemy unless extended.

Common use: forms the input features for traditional machine learning models such as logistic regression, support vector machines, and Naive Bayes. It is often used as a baseline against which more sophisticated representations are compared.