Practical Text Analytics: Maximizing the Value of Text Data

Description

This book introduces text analytics as a valuable method for deriving insights from text data. Unlike other text analytics publications, Practical Text Analytics: Maximizing the Value of Text Data makes technical concepts accessible to those without extensive experience in the field. Using text analytics, organizations can derive insights from content such as emails, documents, and social media. Practical Text Analytics is divided into five parts. The first part introduces text analytics, discusses the relationship with content analysis, and provides a general overview of text mining methodology. In the second part, the authors discuss the practice of text analytics, including data preparation and the overall planning process. The third part covers text analytics techniques such as cluster analysis, topic models, and machine learning. In the fourth part of the book, readers learn about techniques used to communicate insights from text analysis, including data storytelling. The final part of Practical Text Analytics offers examples of the application of software programs for text analytics, enabling readers to mine their own text data to uncover information. Front Matter ....Pages i-xxviii Introduction to Text Analytics (Murugan Anandarajan, Chelsey Hill, Thomas Nolan)....Pages 1-11 Front Matter ....Pages 13-13 The Fundamentals of Content Analysis (Murugan Anandarajan, Chelsey Hill, Thomas Nolan)....Pages 15-25 Planning for Text Analytics (Murugan Anandarajan, Chelsey Hill, Thomas Nolan)....Pages 27-41 Front Matter ....Pages 43-43 Text Preprocessing (Murugan Anandarajan, Chelsey Hill, Thomas Nolan)....Pages 45-59 Term-Document Representation (Murugan Anandarajan, Chelsey Hill, Thomas Nolan)....Pages 61-73 Front Matter ....Pages 75-75 Semantic Space Representation and Latent Semantic Analysis (Murugan Anandarajan, Chelsey Hill, Thomas Nolan)....Pages 77-91 Cluster Analysis: Modeling Groups in Text (Murugan Anandarajan, Chelsey Hill, Thomas Nolan)....Pages 93-115 Probabilistic Topic Models (Murugan Anandarajan, Chelsey Hill, Thomas Nolan)....Pages 117-130 Classification Analysis: Machine Learning Applied to Text (Murugan Anandarajan, Chelsey Hill, Thomas Nolan)....Pages 131-149 Modeling Text Sentiment: Learning and Lexicon Models (Murugan Anandarajan, Chelsey Hill, Thomas Nolan)....Pages 151-164 Front Matter ....Pages 165-165 Storytelling Using Text Data (Murugan Anandarajan, Chelsey Hill, Thomas Nolan)....Pages 167-175 Visualizing Analysis Results (Murugan Anandarajan, Chelsey Hill, Thomas Nolan)....Pages 177-190 Front Matter ....Pages 191-191 Sentiment Analysis of Movie Reviews Using R (Murugan Anandarajan, Chelsey Hill, Thomas Nolan)....Pages 193-220 Latent Semantic Analysis (LSA) in Python (Murugan Anandarajan, Chelsey Hill, Thomas Nolan)....Pages 221-242 Learning-Based Sentiment Analysis Using RapidMiner (Murugan Anandarajan, Chelsey Hill, Thomas Nolan)....Pages 243-261 SAS Visual Text Analytics (Murugan Anandarajan, Chelsey Hill, Thomas Nolan)....Pages 263-282 Back Matter ....Pages 283-285