In the text analytics industry, categorization is the process of assigning a piece of text to one or more categories from a predefined set. This can be done in a supervised or unsupervised manner.

Categorization can be difficult, depending on the complexity of the text data and the number of categories involved. In some cases, it may be necessary to use a supervised machine learning algorithm to train a model to perform categorization. However, in many cases, simple heuristics or rule-based systems can be used.

Categorization vs. Classification

Categorization is sometimes referred to as classification, though there are some subtle differences between the two terms. In general, classification is a more formal term that is used in machine learning, while categorization is a more general term that can be used in any context where items are sorted into groups.

Categorization vs. other Related Terms

Categorization is similar to other sorting tasks, such as clustering and taxonomy. However, unlike these other tasks, categorization always involves predefined categories. This means that the categories must be known in advance, and the goal of categorization is to correctly assign items to these categories.

Common Approach to Categorization

There are a few different ways to approach categorization. One common approach is to look at the frequency of words in each category and use this information to assign new items to categories. Another approach is to use semantic analysis to examine the meaning of words and phrases. This can be used to identify which category an item should belong to, based on the words that are used.


There are many applications for categorization, such as topic modeling and sentiment analysis. In topic modeling, documents are typically categorized into topics, while in sentiment analysis, sentences or phrases are categorized as positive, negative, or neutral.

Categorization is a powerful tool for understanding and organizing text data. It can be used to find trends, extract meaning, and build models. However, it is important to remember that categorization is only as good as the categories that are used. If the categories are not well-defined, the results of categorization will be less accurate.

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