What is the primary goal of tokenization in text processing?

The primary goal of tokenization in text processing is to split text into individual terms. Tokenization serves as a foundational step in natural language processing (NLP) and text analysis. By breaking down paragraphs, sentences, or entire documents into smaller, manageable units—often words or phrases—tokenization allows subsequent processes to analyze and understand the structure and meaning of the text more effectively.

Once the text is tokenized, it can be more easily manipulated for tasks such as text classification, sentiment analysis, or summarization because the individual tokens serve as the basic units of data. This step is crucial for building various NLP models and systems since it helps simplify and clarify the raw text data for further analytical processes. Thus, the importance of tokenization lies in its ability to prepare the text for deeper examination and processing.