May 1, 2024
Updated May 9, 2025
19 minute read
Sentiment analysis, at its core, is the automated process of identifying and categorizing opinions expressed in a piece of text, determining whether the writer's attitude towards a particular topic, product, service, or event is positive, negative, or neutral. Often referred to as opinion mining, this fascinating field blends natural language processing (NLP), text analysis, and computational linguistics to uncover the emotional tone underlying digital communication. In an era overflowing with digital text – from social media comments and product reviews to customer support chats and news articles – sentiment analysis provides a powerful lens to understand public and individual perspectives at scale.
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Find a path to becoming a Sentiment Analysis. Learn more at:
OpenCourser.com/topic/5y8yzq/sentiment
Reading list
We've selected 24 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
Sentiment Analysis.
Is written by one of the pioneers in sentiment analysis, Bing Liu. It provides a systematic overview of opinion mining and sentiment analysis, including techniques for sentiment extraction, sentiment classification, and opinion summarization.
Considered a key book specifically focused on sentiment analysis, this resource offers a comprehensive introduction from an NLP perspective. It covers core areas, including opinion mining, and has been updated to include recent deep learning methods. is valuable for researchers and practitioners and serves as a great introductory and reference book.
Provides a comprehensive overview of sentiment analysis and opinion mining, covering both theoretical foundations and practical applications. It includes chapters on topics such as sentiment lexicons, deep learning approaches, and multimodal sentiment analysis.
This widely-referenced and highly recommended textbook that provides a comprehensive introduction to the field of NLP, with dedicated sections relevant to sentiment analysis. It is suitable for undergraduate and graduate students and serves as a foundational text covering fundamental algorithms and applications.
This revised edition focuses specifically on using the Hugging Face Transformers library, which is widely used for state-of-the-art NLP models, including those for sentiment analysis. It highly practical book for implementing modern sentiment analysis techniques.
Focuses on sentiment analysis specifically within the context of social networks, addressing the unique challenges of analyzing large-scale, noisy, and dynamic text data from platforms like Twitter and Facebook. It explores both semantic and machine learning approaches and provides insights into opinion spamming and social network analysis.
Given the increasing importance of transformers and pre-trained models like BERT in modern NLP, including sentiment analysis, this book is highly relevant for understanding these contemporary techniques. It provides a practical guide to building NLP applications using these architectures.
Provides a comprehensive overview of sentiment analysis, covering its theoretical foundations and empirical approaches. It explores different types of sentiment analysis, such as aspect-based sentiment analysis and emotion analysis.
Focuses on AI-based sentiment analysis methods, providing detailed technical coverage and real-world case studies. It aids in developing practical AI-based sentiment analysis systems and includes concepts, technical explanations, and applications.
Practical guide to sentiment analysis, covering both supervised and unsupervised learning methods. It includes hands-on exercises and real-world case studies, making it a valuable resource for practitioners and researchers alike.
Focuses on the application of neural network models to NLP tasks, which are highly relevant to modern sentiment analysis. It covers the basics of supervised machine learning and progresses to more specialized neural network architectures used in NLP. This valuable resource for those wanting to understand the deep learning aspects of sentiment analysis.
Presents an overview of state-of-the-art deep learning techniques applied to various NLP tasks, including sentiment analysis. It good resource for understanding how deep learning architectures are utilized in the field.
Focuses on the practical application of NLP in real-world scenarios, including use cases relevant to sentiment analysis. It covers the NLP pipeline and how it is used in various sectors, providing valuable context for how sentiment analysis is applied beyond theoretical concepts.
Offers a practical approach to text analysis using Python libraries, relevant for implementing sentiment analysis systems. It focuses on applying machine learning to text data and building data products.
Provides a practical, hands-on introduction to NLP using the NLTK library in Python. While not solely focused on sentiment analysis, it covers essential text processing techniques and concepts that are fundamental for building sentiment analysis systems. It is an excellent resource for beginners wanting to apply NLP methods.
Provides a practical introduction to text mining using the R programming language. It covers sentiment analysis as one of the key applications of text mining, along with other topics such as topic modeling and text classification.
Focuses on building practical NLP applications using Python and popular libraries. It includes creating a sentiment analyzer as one of its examples, making it directly relevant for those who want to implement sentiment analysis systems.
This classic textbook provides a strong theoretical foundation in statistical methods for NLP. While published some time ago, many of the core concepts are still relevant for understanding the statistical underpinnings of sentiment analysis techniques. It is more valuable as a foundational reference than for contemporary methods.
This comprehensive handbook offers a broad overview of NLP techniques and applications. It includes sections on classical and statistical approaches, making it a useful reference for understanding various methods applicable to sentiment analysis. The second edition includes a multilingual scope and greater emphasis on statistical approaches.
As sentiment analysis key application of text mining, this book provides practical methods for analyzing text data. It offers a good foundation in the techniques used to extract information and patterns from text, which are essential for sentiment analysis.
Provides a broad introduction to information retrieval, covering concepts like text classification and indexing that are foundational to many NLP tasks, including aspects of sentiment analysis. While not solely focused on sentiment analysis, it offers valuable background knowledge.
While not exclusively about NLP or sentiment analysis, this book provides a strong foundation in deep learning concepts and their implementation using Python and Keras. Deep learning is crucial for many modern sentiment analysis techniques. It valuable prerequisite or supplementary read for understanding the models used.
Covers text mining techniques with a focus on practical applications using SAS. While SAS-specific, the fundamental concepts of text mining and analysis are highly relevant to sentiment analysis, which common task in text mining.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/5y8yzq/sentiment
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