Understanding Bag-of-Words and corpora. Dictionary in Python

Discover how the Bag-of-Words model can be applied and visualised in a Python project.

- Dr Dipankar Ray

The Bag-of-Words (BoW) model is a fundamental technique for text processing and natural language processing (NLP). It considers each word as an integer number and assigns the frequency of its occurrences within a document constituting the corpus.

In Python, the Gensim library provides powerful tools to work with Bow models, including the 'corpora. Dictionary' class. In rulebased NLP, BOW is an intermediate stage where it receives data from the preliminary text processing steps like cleaning, removal of stopwords and punctuations, and so on, and then provides this data for high-level analysis to understand the corpus. Let's explore how the BoW approach is applied and visualised in a Python project.

The corpora. Dictionary class in Gensim is a mapping between words and their integer IDs. It helps to create the Bow representation of text documents, which is essential for many NLP tasks, including topic modelling.

You can create a dictionary as follows: