Structured Content is defined as content that can be parsed and analyzed by a computer for further processing. This type of content is typically found in databases or other types of electronic records, and it can be contrasted with unstructured content, which cannot be easily parsed or processed by a computer.
There are several benefits to using Structured Content in text analytics. First, it allows for more accurate and consistent results, since the data is organized in a predefined way. Second, it can be easier and faster to analyze, since the computer can more easily identify patterns and trends. Finally, structured content can provide context that might be otherwise unavailable, such as metadata about when a document was created or who created it.
While Structured Content is most often used in the context of text analytics, the term can also be used more broadly to refer to any type of content that can be parsed and analyzed by a computer. For example, structured data formats like XML and JSON are sometimes referred to as “structured content.” In general, though, the term is most often used in the specific context of text analytics.
Structured Content is designed to be parsed and analyzed by a computer, which means that it can be more easily processed by a machine. However, this does not mean that it is not also intended for human consumption. In fact, Structured Content often provides context and metadata that can be helpful for humans who are trying to understand a text.
How is Structured Content Analyzed?
There are several ways to analyze Structured Content. One common approach is to use a text parser, which is a software program that can identify the structure of a piece of text and extract information from it. Another approach is to use natural language processing (NLP) algorithms, which are designed to understand human language and extract meaning from it. Finally, some people may simply manually examine the text to look for patterns and trends.