automatic content classification

Automatic content classification is a process for managing text and unstructured information by categorizing or clustering text. By labeling natural language texts with relevant categories from a predefined set, automatic document classification enables users to organize content quickly and efficiently.

While manual document classification may be highly detailed and accurate, it is time-consuming and subjective. Automatic document classification is faster, scalable and more objective. It provides organizations with a more systematic and consistent classification and can be useful in more complex, nuanced contexts, such as business-specific content. Machine learning and artificial intelligence can boost the speed and efficiency of automatic document classification.

The automated classification of texts into predefined categories has gained attention in the past 10 to 15 years due to the increased availability of documents in digital form and the need to get them organized. Today, text classification is applied in many contexts, including document filtering, email spam filtering, automated document metadata generation, word sense disambiguation and hierarchical catalogs of web resources.

Because automatic document classification software defines the requirements for organizing content at the outset, there needs to be a clear, objective configuration of the categories and classification rules before testing, customization  and  refinement can be performed. Key elements of text classification include the ability to analyze the intent, emotion and sentiment of textual data.

Text classification helps companies understand customer behavior by categorizing conversations on social networks, comment sections  and other web sources. Having an effective and consistent automatic content classification system can provide better customer relationship management (CRM), enhance findability for key audiences and improve and organization's ability to monetize customer-generated information.