May 1, 2024
Updated May 9, 2025
34 minute read
Text classification, at its core, is the process of assigning predefined categories or labels to text. Think of it as a sophisticated sorting system for words, sentences, and documents. This capability is a cornerstone of Natural Language Processing (NLP), a field of artificial intelligence that focuses on enabling computers to understand and process human language. The goal is to take unstructured text data—which makes up a vast majority of the information businesses generate daily—and organize it in a meaningful way. This allows for efficient analysis and retrieval of information, transforming raw text into actionable insights.
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Reading list
We've selected 26 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Text Classification.
Written by creators from Hugging Face, this book practical guide to using the popular Transformers library for various NLP tasks, including text classification. It's highly relevant to contemporary practices and provides hands-on examples for implementing transformer models. It's a must-read for anyone working with modern NLP.
This widely referenced and highly recommended textbook for gaining a broad understanding of NLP, including foundational concepts relevant to text classification. It covers both classic and more modern techniques, making it a valuable resource for students and researchers. While comprehensive, some of the later chapters might delve into areas beyond just text classification. It is commonly used as a textbook in university courses.
This comprehensive book from authors with considerable expertise in this topic provides a comprehensive review of text classification and explores subtopics like text preprocessing, feature extraction, machine learning algorithms, and evaluation. It is highly relevant for gaining a practical understanding of this field.
Dives into the contemporary topic of Transformer models, which are highly relevant to modern text classification. It covers popular models like BERT and their implementation using Python, PyTorch, and TensorFlow. It's an excellent resource for those looking to understand and apply state-of-the-art techniques.
Provides a practical introduction to transformer models like BERT and GPT for solving modern NLP tasks, including text classification. It focuses on providing a functional understanding and how to apply these models in practice. It's suitable for those who want to quickly get up to speed with state-of-the-art models.
This textbook provides a technical perspective on NLP, emphasizing contemporary data-driven approaches and machine learning techniques relevant to text classification. It's suitable for advanced undergraduate and graduate-level courses and can serve as a reference for researchers and data scientists. It requires a background in programming and college-level mathematics.
Offers a practical guide to building end-to-end NLP applications, covering the entire project lifecycle. It includes task-specific case studies and discusses text classification within the context of real-world systems. It's a valuable resource for practitioners and students interested in applied NLP.
Focuses on applying TensorFlow to solve NLP problems, including text classification. It covers fundamental concepts and gradually moves to more advanced models like transformers. It's a practical guide for developers and data scientists familiar with Python and TensorFlow who want to implement NLP solutions.
Provides a practical guide to applying NLP, machine learning, and deep learning to build real-world solutions, with a focus on text processing, analytics, and classification. It covers the entire pipeline from data extraction to model deployment and discusses applications across various industries. It's suitable for students and professionals.
Similar to the TensorFlow book, this resource focuses on implementing NLP tasks, including text classification, using the PyTorch library. It's suitable for those with a background in Python and deep learning who prefer using PyTorch. It provides hands-on examples for building language applications.
This comprehensive textbook offers a deep dive into machine learning techniques for language and speech processing, including text classification. It covers both theoretical concepts and practical applications, making it suitable for advanced learners and practitioners.
Offers a practical approach to text analysis using Python, focusing on building real-world applications. It covers various techniques relevant to text classification within a machine learning context, making it suitable for those who want to apply their knowledge to practical problems. It's a good resource for industry professionals and advanced students.
This advanced book provides a rigorous mathematical and statistical treatment of NLP, including text classification. It is suitable for individuals with a strong background in mathematics and statistics who seek a deep understanding of the underlying principles and algorithms.
Takes a hands-on approach to NLP using Python, covering various tasks including text classification. It focuses on practical implementation and building real-world applications, making it suitable for developers and practitioners.
Focuses specifically on neural network methods for NLP, providing a deeper dive into the models that are crucial for modern text classification. It's suitable for those with a solid understanding of NLP basics and who want to specialize in neural approaches.
Provides a practical and accessible introduction to NLP using the NLTK library in Python. It's excellent for beginners and those who want to gain hands-on experience with fundamental NLP tasks, including text classification. It's often used as a textbook and great resource for getting started with coding examples.
Provides practical recipes for building deep learning models for NLP applications, including text classification. It offers hands-on examples and code snippets, making it a useful resource for practitioners and those who learn by doing.
Covers fundamental concepts in information retrieval, many of which are highly relevant to text classification, such as text representation, indexing, and evaluation. It provides a solid basis for understanding how text data is processed and organized for various tasks. It widely used textbook in computer science programs.
This practical book covers a wide range of machine learning topics, including techniques applicable to text classification. It provides hands-on examples using popular libraries like Scikit-Learn, Keras, and TensorFlow, making it suitable for those who want to implement text classification models. It's a good resource for practitioners.
Focuses on using R for text classification and other text mining tasks. It provides practical guidance, code examples, and case studies. While it may not cover the theoretical aspects as extensively as other books, it valuable resource for practitioners who want to apply text classification in R.
Offers a comprehensive and practical guide to text classification using Python. It covers various algorithms, model evaluation, and case studies. It is an excellent resource for practitioners and beginners seeking to apply text classification in their projects.
Explores specialized text classification techniques for sentiment analysis in social media data. It covers methods for handling the unique challenges of social media text and provides insights into best practices. It is valuable for researchers and practitioners interested in this specific domain.
Considered a classic in the field, this book provides a strong theoretical foundation in statistical methods for NLP. While it predates deep learning, the principles covered are essential for a deep understanding of many text classification techniques. It is more valuable as background reading for advanced students and researchers.
For more information about how these books relate to this course, visit:
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