This second edition textbook covers a coherently organized framework for text analytics which integratesampnbspmaterial drawn from the intersecting topics of information retrieval machine learning andampnbspnatural language processing. Particular importance is placed on deep learning methods. Theampnbspchapters of this book span three broad categories1. Basic algorithms Chapters 1 through 7 discuss the classical algorithms for text analytics such as preprocessing similarity computation topic modeling matrix factorization clustering classification regression and ensemble analysis.2. Domainsensitive learning and information retrieval Chapters 8 and 9 discuss learning models in heterogeneous settings such as a combination of text with multimedia or Web links. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods.ampnbsp3. Natural language processing Chapters 10 through 16 discuss various sequencecentric and natural language applications such as feature engineering neural language models deep learning transformers pretrained language models text summarization information extraction knowledge graphs question answering opinion mining text segmentation and event detection.ampnbspCompared to the first edition this second edition textbook which targets mostly advanced level students majoring in computer science and math has substantially moreampnbspmaterial on deep learning and natural language processing. Significant focus isampnbspplaced on topics like transformers pretrained language models knowledge graphsampnbspand question answering.