For example, if you were analyzing a group of job ads, then you might find that the word “Python” comes up often. Tokenizing your text by word allows you to identify words that come up particularly often. They’re the smallest unit of meaning that still makes sense on its own. Tokenizing by word: Words are like the atoms of natural language. Here’s what both types of tokenization bring to the table: When you’re analyzing text, you’ll be tokenizing by word and tokenizing by sentence. It’s your first step in turning unstructured data into structured data, which is easier to analyze. This will allow you to work with smaller pieces of text that are still relatively coherent and meaningful even outside of the context of the rest of the text. By tokenizing, you can conveniently split up text by word or by sentence.
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