Tokenization is a fundamental step in natural language processing, involving the division of text into individual words or tokens. This process is pivotal for making text data more accessible and manageable for analysis.

Key applications that benefit from tokenization include sentiment analysis, topic modeling, and machine learning. These techniques, when applied to tokenized text, can yield significant insights into the underlying themes, sentiments, and patterns present in the text data.

Tokenization's role is not just limited to breaking down text. It serves as a crucial step in standardizing text data for further analytical procedures, thereby making the overall process of natural language processing more efficient and effective. Furthermore, it facilitates the comparison and analysis of different texts by providing a uniform structure of words or tokens as a basis for comparison.