Now that we have our dataset, how are we going to perform data mining on it?
Text-based datasets include books, essays, websites, manuscripts, programming code, and other forms of written expression. All of the algorithms we have seen so far deal with numerical or categorical features, so how do we convert our text into a format that the algorithm can deal with? There are a number of measurements that could be taken.
For instance, average word and average sentence length are used to predict the readability of a document. However, there are lots of feature types such as word occurrence which we will now investigate.
One of the simplest but highly effective models is to simply count each word in the dataset. We create a matrix, where each row represents a document in our dataset and each column represents a word. The value of the cell is the frequency of that word in the document. This is known as the bag-of-words model.
Here's an excerpt from
The Lord of the...