What does text mining focus on extracting from data?

Text mining primarily focuses on extracting meaningful information from unstructured text. This process involves analyzing and processing large volumes of text data to identify patterns, relationships, and insights that can be derived from the textual information. Unlike numerical or structured data, text data is often free-form and lacks a predefined format, which makes extracting relevant insights more complex.

Text mining includes techniques such as natural language processing (NLP), sentiment analysis, and topic modeling, all of which help in breaking down the text into analyzable components. By doing so, text mining can help organizations draw actionable conclusions from customer feedback, social media posts, academic articles, and more, facilitating informed decision-making based on the information derived from these text sources.

The other choices, such as numerical patterns, visual representations, and geographical data trends, focus on different aspects of data analysis that do not pertain to the specific goal of text mining, which is centered around recognizing and interpreting unique meanings from text.