Free-form text is defined as unstructured text that can be of any length and can include any characters. Free-form text is typically used to refer to natural language, such as a document written in English, but it can also refer to other languages.
There are a few other terms that are similar to free-form text, but have slightly different meanings. One example is semi-structured data, which refers to data that has some structure but is not as rigidly structured as data in a database. Another example is unstructured data, which refers to data that does not have a predefined structure. Finally, there is structured data, which refers to data that has a predefined structure, such as data in a database.
Free-form text is different from these other terms because it specifically refers to text that can be of any length and can include any characters. This makes free-form text very versatile, but it also means that it can be more difficult to analyze than other types of data.
Why is free-form text important in Text Analytics?
Free-form text is important in Text Analytics because it is the most common type of data that is used in natural language processing (NLP). NLP is a subfield of Artificial Intelligence (AI) that deals with understanding human language. In order to understand human language, NLP systems need to be able to handle a variety of different sentence structures, grammar rules, and vocabulary. This can be a challenge for NLP systems, but free-form text is the best type of data to use in order to train these systems.
How is free-form text used outside of Text Analytics?
There are many ways that free-form text can be used outside of Text Analytics. One example is customer feedback surveys. Customer feedback surveys often include open-ended questions that allow customers to write their responses in free-form text. This type of data can be very valuable for companies, as it can provide insights into how customers feel about their products or services.
Another way that free-form text can be used outside of Text Analytics is in social media posts. Social media posts are typically short and can include a variety of different characters, such as hashtags, emojis, and abbreviations. This type of data can be difficult to analyze, but it can be very valuable for companies that want to understand how people are talking about their brand on social media.
Finally, free-form text can also be used in academic research. Academic research often includes papers that are written in free-form text. This type of data can be very valuable for researchers, as it can provide insights into a variety of different topics.