7 New Sentiment Analysis Books Reshaping the Industry in 2025

Drawing from expertise in natural language processing, Edward R. Deforest offers a clear guide to sentiment analysis using spaCy that demystifies complex text analysis techniques. You’ll gain hands-on skills to extract opinions and emotional tones from diverse text sources like customer reviews or social media posts, with practical examples illustrating spaCy’s application across industries. The book tackles common concerns about accuracy and usability, making it accessible even if you’re new to coding. If you want to understand public sentiment to inform decisions or strategies, this resource delivers targeted insights without unnecessary jargon or fluff.

Drawing from expertise in computational intelligence, Soni Sweta presents a focused examination of sentiment analysis within educational data mining. You’ll explore how natural language processing, machine learning, and deep learning techniques combine to interpret student feedback and emotional states like stress or boredom. The book guides you through real-world case studies demonstrating how these insights help tailor curriculum design and adaptive learning systems. If you're involved in education or edtech development, this book offers a clear understanding of applying sentiment analysis to enhance student engagement and informed decision-making.

This tailored book explores advanced sentiment analysis techniques, focusing on the latest developments and discoveries up to 2025. It covers foundational concepts, algorithmic enhancements, and emerging tools, offering a rich learning experience that matches your background and interests. The content examines cutting-edge AI models and real-world applications, providing a deep dive into techniques that reveal nuanced emotional insights in text data. By focusing on your specific goals, this personalized guide navigates recent research and evolving practices to keep you ahead in sentiment mastery. It reveals how sentiment analysis evolves within diverse contexts, enabling you to apply knowledge meaningfully and effectively.

Drawing from his extensive academic background, Prof Philip M. Parker Ph.D. offers a global economic perspective on retail sentiment analysis software in this detailed study. You gain a clear understanding of latent demand forecasting across over 190 countries, complete with economic modeling that reveals potential industry earnings and market share insights by region. The book is particularly suited for strategists and analysts interested in long-term market trends rather than product-level details. If you want to grasp the macroeconomic forces shaping retail sentiment analysis software adoption worldwide, this book provides a solid framework without diving into vendor-specific discussions.

When Prof Philip M. Parker Ph.D. crafted this outlook, his econometric expertise guided a global examination of sentiment analysis software markets, focusing on latent demand across more than 190 countries. You gain a strategic perspective on industry potential rather than product specifics, learning to interpret economic indicators and regional market shares that shape sentiment analysis software growth. This is a fit for analysts, strategists, and decision-makers seeking a high-level, data-driven understanding of worldwide market dynamics beyond typical vendor-focused reports. Particularly, chapters detailing country-level demand projections stand out for their practical use in global market assessment.

Chris C. Thompson’s experience with natural language processing illuminates this guide on Hugging Face Transformers, designed to bring sentiment analysis within reach for all skill levels. You’ll learn to prepare data, build models, and deploy solutions using practical Python examples that walk you through real problems like analyzing social media sentiment and product reviews. The book balances foundational concepts with the latest Transformer models, making it a solid choice if you want to bridge the gap between theory and hands-on application. If you’re seeking a deep dive into advanced algorithms, this might feel introductory, but for mastering AI-powered sentiment analysis from scratch, it fits well.

This tailored book on sentiment analysis explores the forefront of innovations shaping the field in 2025 and beyond. It examines emerging research, novel algorithmic approaches, and evolving applications that redefine how sentiment is interpreted from text. By focusing on your interests and background, it reveals the most relevant advancements, helping you stay ahead of new discoveries and technologies. The personalized content matches your goals, offering a deep dive into the trajectory of sentiment analysis, including breakthroughs in AI models, nuanced emotion detection, and adaptive methodologies that respond to shifting data landscapes. This custom guide fosters a rich understanding of where sentiment analysis is headed and how to engage with its latest trends effectively.

D. Jude Hemanth challenges the conventional wisdom that sentiment analysis can rely solely on traditional methods by diving deep into computational intelligence techniques tailored for natural language processing applications. You’ll explore how AI-driven approaches like transfer learning and neural networks tackle complex tasks such as sarcasm detection and emotion recognition, which conventional models often miss. The book doesn’t just theorize; it presents detailed case studies from pandemic medical data and human-computer interaction to illustrate practical implementations. This is ideal if you’re a computer scientist or AI practitioner aiming to build robust, real-world sentiment analysis systems that handle the nuances of human language.

Jessica Olivares López's expertise in deep learning and natural language processing shapes this focused exploration of sentiment analysis applied to unstructured text. The authors delve into how sentiment polarity—positive, negative, or neutral—can be automatically assigned to sentences and documents, unlocking insights from vast, daily-generated datasets across industries like textile, automotive, and government. You’ll gain a clear understanding of practical applications for validating and supporting decision-making through data-driven inference, with examples highlighting how sentiment analysis integrates into real-world sectors. This concise volume suits professionals eager to apply AI techniques to interpret human emotions in text, though its brevity means it’s best as an introduction rather than an exhaustive reference.