An Introduction to AI for Text Mining: A Companion to the Evisort Case Study

An Introduction to AI for Text Mining: A Companion to the Evisort Case

This document provides a comprehensive overview of "An Introduction to AI for Text Mining: A Companion to the Evisort Case," a case study by C. Daniel Guetta, published on May 3, 2019. The case study, identified by product number CU253 and spanning 24 pages, delves into the practical applications and theoretical underpinnings of AI-driven text mining within the business analytics landscape, specifically focusing on its relevance to the legal services industry.

Understanding AI-Driven Text Mining

AI-driven text mining is presented as a novel business analytics tool that empowers users to extract valuable insights from vast amounts of unstructured data, such as documents. It enables the searching and analysis of information based on both content and associated metadata. The process of analyzing documents using AI involves several critical stages:

• Optical Character Recognition (OCR): The initial step involves converting document images into machine-readable text. This is crucial for making the content accessible for further analysis.
• Text Formatting: Once converted to text, documents need to be processed into a format suitable for analytical algorithms.
• Information Extraction: The final stage involves extracting meaningful information and patterns from the processed text.

Case Study Context: Evisort

This case study serves as a companion to another case, "Evisort: An AI-Powered Start-up Uses Text Mining to Become Google for Contracts" (Case ID: CU251). Evisort is highlighted as an AI-powered startup that leverages text mining to revolutionize contract management, effectively becoming a search engine for legal documents.

Key Concepts and Applications

The case study explores the theory behind text analysis and provides practical examples of its application. It emphasizes how AI and machine learning techniques can unlock the potential of large document repositories, making them searchable and analyzable in ways previously impossible.

Target Audience and Industry Relevance

The content is particularly relevant to professionals and students in fields such as:

• AI and Machine Learning: Understanding the core technologies and their implementation.
• Product Launches and Development: Analyzing how AI can inform product strategy.
• Operations Strategy and IT Management: Optimizing business processes through data analysis.
• Start-ups and Entrepreneurial Management: Leveraging AI for competitive advantage.

The case study is situated within the "Legal services industry," indicating its direct applicability to law firms, corporate legal departments, and legal technology providers.

Product Details and Purchasing Information

• Product Number: CU253
• Pages: 24
• Publication Date: May 03, 2019
• Source: Columbia Business School
• MSRP: $8.95 (USD)

Available Formats and Languages

The product is available in various formats, including PDF, Audio (MP3, M4A, CDROM, Cassette), Bundle, DVD, Event Live/Virtual Conference, Word Document, Electronic Book, ePub, Financial, Hardcover/Hardcopy (Color and B&W), Web Based HTML, Kit, License, Magazine, Mobi, Multimedia CDROM/Windows Media, Paperback Book (Color and B&W), Powerpoint, Subscription, Service, Video CDROM/DVD/Flash/VHS/Real Player, Microsoft Excel Spreadsheet, XML, and Zip File.

Languages offered include English, Spanish, Chinese, Danish, French, German, Japanese, Portuguese, Polish, Russian, Slovak, and Traditional Chinese.

Copyright and Permissions

Copyrighted PDFs are intended for individual use only. Users can purchase copyright permission to share the PDF with their team, with tiered pricing available for bulk purchases (1-4 copies at List Price, 5-10 at $8.75, 11-49 at $8.50, 50-499 at $8.25, and 500+ at $8.00).

Educational Use

Educators can register as Premium Educators at hbsp.harvard.edu to plan courses and offer students academic discounts of up to 50%.

Related Content

The case study is part of a broader discussion on AI and text mining, with related products including:

• "Evisort: An A.I.-Powered Start-up Uses Text Mining to Become Google for Contracts" (Case ID: CU251)
• "Enhancing Mining Sector Sustainability with AI"
• "Empowering AI Companions for Enhanced Relationship Marketing"

Conclusion

"An Introduction to AI for Text Mining: A Companion to the Evisort Case" offers valuable insights into the practical application of AI in analyzing textual data, providing a foundational understanding for businesses looking to leverage text mining for competitive advantage. The case study highlights the essential steps in text analysis and its impact across various industries, particularly in legal services.