Text mining for knowledge discovery is a process that employs natural language processing (NLP) to extract valuable insights from document collections or databases. As a type of data mining, text mining has evolved into a principal methodology for interpreting large volumes of unstructured information. The primary purpose of text mining is to identify patterns and relationships hidden in the data, subsequently presenting them in an easily consumable form that enables better decision-making.

• Insight Generation: Text mining for knowledge discovery generates insights from vast unstructured data, reducing the human effort needed to sift through extensive documents or databases.
• Pattern Detection: This process incorporates advanced statistical and machine learning techniques to discern trends and patterns within the data, enabling businesses to glean pertinent knowledge.
• High-speed Processing: The application of automated algorithms allows text mining to process large volumes of data in lesser time, enhancing efficiency and productivity.
• Scalable: Text mining techniques can be seamlessly scaled as data volumes increase, ensuring steady processing speeds and dependable results.
• Predictive Analysis: These processes can yield foresight of future circumstances by studying trends and models in past and present data.

The implementation starts with a clear understanding of the business objectives and the type of data involved. Next, the selection of appropriate text mining techniques is essential, followed by cleaning and preprocessing data to remove irrelevant information and errors. This preparatory step is followed by the actual text mining process in the training and validation datasets. Beyond this, refining the models, implementing them, and generating actionable insights is vital. Rigorous monitoring and maintenance ensure the model's effectiveness.

• Data-Driven Decisions: By transforming unstructured data into structured information, text mining provides actionable insights for data-driven decision-making, enhancing business strategies.
• Efficiency: By automating the extraction and analysis of knowledge from unrefined data, text mining improves efficiency by reducing manual effort and time, thus freeing up resources for more critical tasks.
• Competitive Advantage: Insights generated from text mining can provide a competitive edge in various sectors, including healthcare, finance, marketing, and more.
• Risk Management: By identifying trends and patterns, businesses can predict potential risks and prepare suitable responses, ensuring a proactive approach to risk management.
• Customer Satisfaction: Text mining helps predict customer behavior and sentiment, enabling firms to tailor their services or products for optimized customer experience.

• Data Privacy: Mining sensitive data can lead to privacy concerns.
• Quality of Information: The quality of insights generated is heavily reliant on the quality of the input data, highlighting a need for effective data cleaning and preprocessing.
• Complexity: The algorithms and statistical methods used in text mining are complex and require significant expertise to handle effectively.
• Language Limitations: Text mining techniques might struggle with understanding context, irony, sarcasm, or cultural nuances in the content.
• Process Transparency: The 'black box' nature of certain algorithms can make it difficult to interpret exactly how the results were derived.

In conclusion, text mining for knowledge discovery is a potent tool that can unearth valuable insights, offering potential solutions for various industries. Its prolonged utility, however, banks on the careful implementation, monitoring, and maintenance of the system. With adequate foresight and strategic planning, businesses can leverage text mining to their advantage despite potential challenges.