Tokenization is a natural language processing technique that involves breaking down a text or a document into individual words, phrases, symbols, or other meaningful elements, known as tokens. Tokenization is a crucial first step in many natural language processing tasks such as sentiment analysis, part-of-speech tagging, and named entity recognition.
Tokenization involves splitting a document or a sentence into individual tokens, which are the basic units of meaning in the text. The tokens may be words, phrases, or even individual characters, depending on the level of granularity required for the particular task at hand. For example, a document may be tokenized into individual words, while a tweet may be tokenized into individual words and hashtags.
The process of tokenization involves several steps. First, the text is cleaned and preprocessed to remove any unwanted elements such as punctuation, numbers, and special characters. Then, the text is broken down into individual tokens using various techniques such as whitespace splitting, regular expressions, and machine learning algorithms.
Tokenization is a fundamental step in natural language processing because it provides the basic units of meaning that are required for further analysis and processing of the text. By breaking down a document into individual tokens, natural language processing algorithms can perform more advanced tasks such as identifying the parts of speech, analyzing sentiment, and extracting named entities.