LibGuides: Text and Data Mining: Books on TDM

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Books on TDM in NTU Library

Applying Language Technology in Humanities Research
ISBN: 9783030464929

Publication Date: 2020

This book explores how Language Technology—encompassing text mining, computational linguistics, and NLP—can enhance humanities research by enabling the analysis of large textual datasets like Google Books and Project Gutenberg. With practical examples from history, literary studies, and linguistics, it serves as a valuable resource for students and researchers in the digital humanities.
Practical Text Analytics : Maximizing the Value of Text Data
ISBN: 9783319956626

Publication Date: 2019

Practical Text Analytics introduces text analytics in an accessible way, making technical concepts understandable for those without prior experience. Covering methodology, data preparation, analytics techniques, data storytelling, and software applications, the book provides a comprehensive guide to extracting insights from text data such as emails, documents, and social media.
Text Data Mining
ISBN: 9789811600999

Publication Date: 2021

This book explores text data mining from a natural language processing (NLP) perspective, covering fundamental theories and methods such as pre-processing, text representation, classification, and clustering. It also examines key applications, including topic modeling, sentiment analysis, opinion mining, information extraction, and automatic text summarization.
Text Mining : a guidebook for the social sciences
ISBN: 9781483369341

Publication Date: 2017

Text Mining provides strategic and practical guidance on analysing large text collections using both qualitative and quantitative methods. Written by a sociologist and a computer scientist, it surveys data sources, tools, and techniques, making it accessible for researchers at all levels.
Text Mining Approaches for Biomedical Data
ISBN: 9789819739615

Publication Date: 2024

Text Mining Approaches for Biomedical Data explores how AI and machine learning revolutionize healthcare by mining biomedical texts, clinical trials, and literature. It covers text representation techniques, predictive modeling, and deep learning applications, making it a valuable resource for researchers and professionals in healthcare and data science.
Working with Text
ISBN: 9781780634302

Publication Date: 2016

Working with Text explores text mining from cross-disciplinary perspectives, covering legal and ethical considerations, tools like GATE, and applications in fields such as social media analysis, semantic search, and historical text processing. It provides practical insights into text mining’s relevance to information science and digital humanities while offering guidance on developing expertise in the field.

Advanced Data Mining Tools and Methods for Social Computing
ISBN: 9780323857086

Publication Date: 2022

This book explores advanced data mining tools, methods, and algorithms tailored for social computing and social network analysis. It reviews emerging trends, novel techniques, and state-of-the-art advancements in machine learning and soft computing for analysing social networks.
Data Mining : Practical Machine Learning Tools and Techniques, 4th Edition
ISBN: 9780128042915

Publication Date: 2017

Data Mining provides a comprehensive introduction to machine learning concepts and their practical applications in data mining. It teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.
Data Mining and Predictive Analytics
ISBN: 9781118868676

Publication Date: 2015

This book introduces key data mining methods, including clustering, neural networks, and logistic regression, using a "white box" approach for better understanding. Featuring hands-on exercises, real-world case studies, and R programming, it is ideal for students and professionals in computer science, statistics, and business.
Data Science for Business : what you need to know about data mining and data-analytic thinking
ISBN: 9781449361327

Publication Date: 2013

Data Science for Business introduces fundamental data science principles and "data-analytic thinking" for extracting business value from data. Based on an NYU MBA course, it helps readers understand data-mining techniques, improve communication between business stakeholders and data scientists, and apply data-driven decision-making in a competitive business environment.
Introduction to data mining
ISBN: 9781292026152

Publication Date: 2014

Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms.
Learn Data Mining Through Excel
ISBN: 9781484297704

Publication Date: 2023

Learn Data Mining Through Excel teaches data mining techniques using Microsoft Excel, providing a step-by-step approach to understanding machine learning concepts. Designed for Excel users and data science beginners, it covers data manipulation, visualization, and pattern discovery without requiring specialized software or programming.

Machine Learning for Business Analytics
ISBN: 9781119903840

Publication Date: 2023

This book provides a comprehensive introduction to machine learning for business analytics using JMP Pro®, featuring real-world case studies and practical applications. The second edition includes updated content, four new chapters on topics like Text Mining and Responsible Data Science, and a companion website with datasets and instructional resources.
Machine Learning for Data Science Handbook
ISBN: 9783031246272

Publication Date: 2023

This book is a major update, incorporating new topics like deep learning, explainable AI, and big data methods while revising existing chapters to reflect the field's growth. This comprehensive reference covers essential machine learning techniques and serves researchers, practitioners, and students in data science, AI, and related fields.
Machine Learning Methods
ISBN: 9789819939169

Publication Date: 2023

This book is a popular machine learning textbook and reference book in China. It provides a systematic introduction to key machine learning methods, covering both supervised (e.g., classification, regression) and unsupervised learning (e.g., clustering, dimensionality reduction). It is designed for researchers and students in fields like information retrieval and text mining, offering detailed explanations, mathematical derivations, and practical examples.
Text As Data : a new framework for machine learning and the social sciences
ISBN: 9780691207551

Publication Date: 2022

Text as Data provides a guide to using computational text analysis to gain insights into social science, humanities, and industry by integrating machine learning tools with research design. Organised around key research tasks like representation, discovery, and prediction, it bridges disciplines and offers practical methods for analysing large-scale text data.

The Absolute Beginner's Guide to Python Programming
ISBN: 9781484287156

Publication Date: 2022

This beginner-friendly guide introduces Python programming through step-by-step examples, covering language basics, data types, operators, functions, and object-oriented programming. It also explores interface design, game development with PyGame, and web development, making it ideal for beginners, students, and aspiring programmers.
Data Mining for Business Analytics : Concepts, Techniques, and Applications in Python
ISBN: 1119549868

Publication Date: 2020

Data Mining for Business Analytics offers a hands-on approach to applying data mining techniques using Python, covering statistical and machine learning algorithms for prediction, classification, visualization, clustering, and text mining. This sixth edition includes new ethical discussions, real-world case studies, and practical exercises, making it a valuable resource for students, researchers, and professionals in business analytics.
Practical Data Science with R
ISBN: 9781638352747

Publication Date: 2019

This book is a hands-on guide to data science, focusing on real-world applications in business intelligence, marketing, and decision support. Using the R programming language, the book covers statistical analysis, data visualization, predictive modeling, and effective data presentation. Designed for business analysts and data scientists.
Practical Machine Learning in R
ISBN: 9781119591573

Publication Date: 2020

Practical Machine Learning in R introduces professionals and students to machine learning concepts with hands-on examples using the R programming language. Covering topics such as data wrangling, classification, regression, clustering, and model evaluation, the book provides practical guidance for applying AI-driven techniques to real-world business problems.
Python Programming for Data Analysis
ISBN: 9783030689513

Publication Date: 2021

This textbook, based on the ECE143 Programming for Data Analysis course at UC San Diego, is designed for readers with some Python experience who want to deepen their understanding of programming for data analysis. Covering Python memory management, data structures, object-oriented programming, and key libraries like NumPy, Pandas, and Matplotlib, it helps readers develop efficient coding practices and effective data visualizations.
Text Analytics with Python
ISBN: 9781484243534

Publication Date: 2019

This book provides a comprehensive guide to NLP in Python, incorporating machine learning and deep learning for text analytics. It introduces state-of-the-art frameworks for sentiment analysis, topic modeling, text summarization, and named entity recognition (NER). Readers will learn how to engineer text representations, apply statistical and deep learning-based embedding models, and develop real-world applications like movie recommenders and sentiment analysis tools.
Text Mining with R
ISBN: 9781491981658

Publication Date: 2017

This book introduces text-mining techniques using tidytext, an R package developed with the tidy principles behind tools like ggraph and dplyr. It demonstrates how treating text as data frames makes analysis more accessible and effective. Key topics include sentiment analysis, frequency measurement, topic modeling, and visualizing word relationships.

Becoming a Data Head
ISBN: 9781119741763

Publication Date: 2021

Becoming a Data Head provides a practical and engaging introduction to data science, statistics, and machine learning, helping readers develop critical thinking skills and confidently discuss data-driven topics. The book covers key concepts such as statistical reasoning, machine learning fundamentals, and common pitfalls in data interpretation, making it an accessible resource for professionals across industries.
The beginner's guide to data science
ISBN: 9783031078651

Publication Date: 2022

This book provides an in-depth exploration of data science principles and applications, covering topics such as data wrangling, machine learning, statistics, natural language processing, and time series analysis. It also examines real-world challenges like data gathering, building recommendation engines, and utilizing metrics for distance-based analysis, offering comprehensive code examples and case studies to help readers understand and apply these techniques effectively.
Data Science for Business and Decision Making
ISBN: 9780128112175

Publication Date: 2019

This book integrates statistics and operations research to provide a comprehensive foundation in business analytics, with a focus on regression, optimization, and simulation. It includes exercises, real-world datasets, and guidance on using software like Excel, SPSS, and Stata for data-driven decision-making.