What does text mining primarily involve?

Text mining primarily involves extracting high-quality information from text. This process includes analyzing unstructured data sources like documents, emails, and social media content to derive meaningful insights. Text mining employs various techniques such as natural language processing, machine learning, and statistical analysis to identify patterns, trends, and relationships within the text.

The ability to sift through vast amounts of information and distill it into actionable insights is what makes text mining particularly valuable in fields such as marketing, healthcare, and social sciences, where textual data is abundant. By focusing on extracting valuable insights from the text, text mining enables organizations to leverage information that can improve decision-making and strategy.

Visualizing data trends pertains more to data visualization techniques, which represent data in graphical formats rather than deriving insights from text. Deriving information from structured data is a different process altogether, primarily dealing with quantitative data organized in predefined formats. Building databases for textual information focuses on data organization rather than the analytical process of extracting knowledge. Hence, the correct answer encapsulates the essence of what text mining aims to achieve.