<p>Text analytics is a field that lies on the interface of information retrieval,machine learning, and natural language processing, and this textbook&nbsp;carefully covers a coherently organized framework drawn from these intersecting&nbsp;topics. The chapters of this textbook is organized into three categories:</p><p><b><i>- Basic algorithms: </i></b>Chapters 1 through 7 discuss the classical algorithms&nbsp;for machine learning from text such as preprocessing, similarity&nbsp;computation, topic modeling, matrix factorization, clustering,&nbsp;classification, regression, and ensemble analysis.</p><p><b><i>- Domain-sensitive mining: </i></b>Chapters 8 and 9 discuss the learning methods&nbsp;from text when combined with different domains such as multimedia and&nbsp;the Web. The problem of information retrieval and Web search is also&nbsp;discussed in the context of its relationship with ranking and machine&nbsp;learning methods.&nbsp;</p><p><b><i>- Sequence-centric mining: </i></b>Chapters 10 through 14 discuss various&nbsp;sequence-centric and natural language applications, such as feature&nbsp;engineering, neural language models, deep learning, text summarization,&nbsp;information extraction, opinion mining, text segmentation, and event&nbsp;detection.</p><p>&nbsp;This textbook covers machine learning topics for text in detail. Since the&nbsp;coverage is extensive,multiple courses can be offered from the same book,&nbsp;depending on course level. Even though the presentation is text-centric,&nbsp;Chapters 3 to 7 cover machine learning algorithms that are often used indomains beyond text data. Therefore, the book can be used to offer&nbsp;courses not just in text analytics but also from the broader perspective of&nbsp;machine learning (with text as a backdrop).</p><p>&nbsp;This textbook targets graduate students in computer science, as well as researchers, professors, and industrial&nbsp;practitioners working in these related fields. This textbook is accompanied with a solution manual for&nbsp;classroom teaching.</p>