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.
Working in sentiment analysis can be particularly engaging due to its direct impact on understanding human expression and its broad applicability across diverse industries. Imagine the excitement of developing algorithms that can detect sarcasm or subtle shifts in opinion within online discussions, or the satisfaction of helping a company improve its products by systematically analyzing customer feedback. The field is also constantly evolving, with new techniques and challenges emerging regularly, offering a continuous learning experience for those intellectually curious and driven by innovation.
Core Concepts and Terminology
To truly appreciate sentiment analysis, it's helpful to understand some of its fundamental concepts and the language used by practitioners. These building blocks provide the framework for more advanced understanding and application.
Polarity, Subjectivity, and Objectivity
At the heart of sentiment analysis lies the concept of polarity, which classifies the sentiment expressed in a text as positive, negative, or neutral. For example, a movie review stating "This film was absolutely brilliant!" exhibits positive polarity, while "I was incredibly disappointed by the plot" shows negative polarity. A neutral statement might be "The movie is 90 minutes long."
5y8yzq|
Find a path to becoming a Sentiment Analysis. Learn more at:
OpenCourser.com/topic/5y8yzq/sentiment
Reading list
We've selected 15 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.
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.
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.
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.
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.
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.
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
Save
