Machine Learning for Text

by Charu C. Aggarwal

1 An Introduction to Text Analytics.- 2 Text Preparation and Similarity Computation.- 3 Matrix Factorization and Topic Modeling.- 4 Text Clustering.- 5 Text Basic Models.- 6 Linear Models for Classification and Regression.- 7 Classifier Performance and Evaluation.- 8 Joint Text Mining with Heterogeneous Data.- 9 Information Retrieval and Search Engines.- 10 Text Sequence Modeling and Deep Learning.- 11 Text Summarization.- 12 Information Extraction.- 13 Opinion Mining and Sentiment Analysis.- 14 Text Segmentation and Event Detection.

520 pages, Paperback

Published March 22, 2018

Reviews

[A Mig · Rating 5 out of 5]

Where to start to learn about text classification and clustering. Provides practical guidance, with some nice toy examples to illustrate some of the algorithms' intricacies. The software resources, along with the bibliographical notes, were quite useful. As a plus, the quotes at the start of each chapter were spot-on.

[Aryan kamboj · Rating 5 out of 5]

Being an avid reader of Charu C. Aggarwal's books, I had to read this book. I was working on a project on Natural Language Processing when I got a chance to read this book. Needless to say, it is a classic. Unlike many other books written on the topic, Aggarwal's book stands out. The thorough explanation of even the smallest topic and "why?" of the various algorithms was very helpful, instead of just throwing the topics on the reader, the author took to things one by one, explaining the reason for why are we doing this and why we need improvement. I have read many articles and blogs on LDA and PLSA but never understood the sole reason behind the development of both. The author clearly explained why it was necessary and also made sure that the mathematical and theoretical balance remains. I managed to read the whole book in a couple of sittings. Most of the times, authors do not consider it important to include Information Retrieval as a part of Natural Language Processing, but Mr. Aggarwal included a whole chapter on Retrieval Methods. These are just a few parts of the book, in all, the whole book is an ocean of information and I would definitely recommend this book to anyone who wants to completely understand NLP and its latest advancements.