Text as Data: Computational Methods of Understanding Written Expression Using SAS

Text As Data: Combining qualitative and quantitative algorithms within the SAS system for accurate, effective and understandable text analytics

The need for powerful, accurate and increasingly automatic text analysis software in modern information technology has dramatically increased. Fields as diverse as financial management, fraud and cybercrime prevention, Pharmaceutical R&D, social media marketing, customer care, and health services are implementing more comprehensive text-inclusive, analytics strategies. Text as Data: Computational Methods of Understanding Written Expression Using SAS presents an overview of text analytics and the critical role SAS software plays in combining linguistic and quantitative algorithms in the evolution of this dynamic field.

Drawing on over two decades of experience in text analytics, authors Barry deVille and Gurpreet Singh Bawa examine the evolution of text mining and cloud-based solutions, and the development of SAS Visual Text Analytics. By integrating quantitative data and textual analysis with advanced computer learning principles, the authors demonstrate the combined advantages of SAS compared to standard approaches, and show how approaching text as qualitative data within a quantitative analytics framework produces more detailed, accurate, and explanatory results.

• Understand the role of linguistics, machine learning, and multiple data sources in the text analytics workflow
• Understand how a range of quantitative algorithms and data representations reflect contextual effects to shape meaning and understanding
• Access online data and code repositories, videos, tutorials, and case studies
• Learn how SAS extends quantitative algorithms to produce expanded text analytics capabilities
• Redefine text in terms of data for more accurate analysis

This book offers a thorough introduction to the framework and dynamics of text analytics--and the underlying principles at work--and provides an in-depth examination of the interplay between qualitative-linguistic and quantitative, data-driven aspects of data analysis. The treatment begins with a discussion on expression parsing and detection and provides insight into the core principles and practices of text parsing, theme, and topic detection. It includes advanced topics such as contextual effects in numeric and textual data manipulation, fine-tuning text meaning and disambiguation. As the first resource to leverage the power of SAS for text analytics, Text as Data is an essential resource for SAS users and data scientists in any industry or academic application.

《Text As Data: 在 SAS 系統中結合定性和定量演算法進行準確、有效和易於理解的文字分析》

現代資訊科技中，對於強大、準確且越來越自動化的文字分析軟體的需求急劇增加。金融管理、詐騙和網絡犯罪預防、藥物研發、社交媒體營銷、客戶服務和健康服務等領域正在實施更全面的文字分析策略。《Text as Data: Computational Methods of Understanding Written Expression Using SAS》概述了文字分析的概念，以及 SAS 軟體在結合語言和定量演算法方面在這一動態領域中所扮演的關鍵角色。

作者 Barry deVille 和 Gurpreet Singh Bawa 基於二十多年的文字分析經驗，探討了文字採礦和基於雲端的解決方案的演進，以及 SAS Visual Text Analytics 的發展。通過將定量數據和文字分析與先進的計算機學習原則相結合，作者展示了 SAS 相對於標準方法的優勢，並展示了在定量分析框架中將文字作為定性數據來處理可以產生更詳細、準確和解釋性的結果。

本書包括以下內容：
- 理解語言學、機器學習和多種數據來源在文字分析工作流程中的作用
- 理解一系列定量演算法和數據表示如何反映上下文效應以形成意義和理解
- 存取在線數據和程式碼庫、視頻、教程和案例研究
- 學習 SAS 如何擴展定量演算法以提供更多的文字分析能力
- 重新定義文字以進行更準確的分析

本書全面介紹了文字分析的框架和動態，以及其中的基本原則和實踐，並深入探討了定性語言學和定量數據驅動的數據分析之間的相互作用。內容從表達解析和檢測開始，提供了有關文字解析、主題和話題檢測的核心原則和實踐的見解。還包括了數字和文字數據操作中的上下文效應、微調文字意義和消除歧義等高級主題。作為首個利用 SAS 進行文字分析的資源，《Text as Data》是 SAS 用戶和任何行業或學術應用中的數據科學家的重要資源。