Bag of words | Text Mining | Quantra by QuantInsti
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Timestamp:
00:14 - 00:29 - What is Bag of Words?
00:29 - 00:43 - How does the bag of words work?
00:43 - 01:29 - Example to understand this method
Welcome to this video lesson on Bag of Words. After completing this, you will be able to: Explain the bag-of-words (BoW) model. Construct a word table from several documents based on the BoW method.
What is Bag of Words?
It is an algorithm that counts how many times a word appears in a document. Those word counts allow one to compare documents and gauge their similarities for applications like search and document classification. How Does the Bag of Words Work? Bag of words lists words paired with their word counts per document. In the table each row is a document, each column is a word, and each cell is a word count.
Let’s learn this method through an example. Consider the following three sentences. Applying the Bag of words to this set, we get a table with three rows. We can see in the table that the word love appears only in the first document, hate only appears in the second, hobby and passion only appear in the third.
Using the Bag of words model, we can identify the important words – that is the signature words in the different documents by visual inspection. Before they’re fed to the neural network, each vector of word counts is normalized such that all elements of the vector add up to one. Thus, the frequency of each word is effectively converted to represent the probabilities of those words’ occurrences in the document. Probabilities that surpass certain levels will activate nodes in the network and influence the document’s classification. In the next unit, you will learn about the limitations of Bag of words and more sophisticated methods Term Frequency-Inverse Document Frequency (TF-IDF).
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