Text Analytics Is A Field That Lies On The Interface Of Information Retrieval,machine Learning, And Natural Language Processing, And This Textbookcarefully Covers A Coherently Organized Framework Drawn From These Intersectingtopics. The Chapters Of This Textbook Is Organized Into Three Categories:- Basic Algorithms: Chapters 1 Through 7 Discuss The Classical Algorithmsfor Machine Learning From Text Such As Preprocessing, Similaritycomputation, Topic Modeling, Matrix Factorization, Clustering,classification, Regression, And Ensemble Analysis.- Domain-sensitive Mining: Chapters 8 And 9 Discuss The Learning Methodsfrom Text When Combined With Different Domains Such As Multimedia Andthe Web. The Problem Of Information Retrieval And Web Search Is Alsodiscussed In The Context Of Its Relationship With Ranking And Machinelearning Methods.- Sequence-centric Mining: Chapters 10 Through 14 Discuss Varioussequence-centric And Natural Language Applications, Such As Featureengineering, Neural Language Models, Deep Learning, Text Summarization,information Extraction, Opinion Mining, Text Segmentation, And Eventdetection.this Textbook Covers Machine Learning Topics For Text In Detail.