Sentiment Analysis Unveiled
Techniques, Applications, and Innovations
- 200 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Sentiment Analysis Unveiled
Techniques, Applications, and Innovations
About this book
This book is a comprehensive exploration into the realm of sentiment analysis. From deciphering customer sentiments for businesses to understanding public opinions on social media or predicting market trends, the applications are multifaceted and impactful.
Sentiment Analysis Unveiled: Techniques, Applications, and Innovations is more than just algorithms and models; it's about unraveling the emotions, opinions, and perceptions encapsulated within the vast sea of textual data. This book explores topics from opinion mining, social media analysis, deep learning, security concerns, and healthcare systems, and it also delves into the ethical and legal implications of sentiment analysis. Through practical examples, case studies, and discussions on cutting?edge innovations, the editors aim is to provide a holistic view that empowers you to navigate this field confidently. It involves the analysis of user?generated content, deciphering sentiments expressed on platforms like Twitter and Facebook, and provides valuable insights into public opinion, brand perception, and emerging trends in the digital landscape.
This book is intended for professionals, researchers, and scientists in the field of artificial intelligence and sentiments analysis; it will serve as a valuable resource for both beginners and experienced professionals in the field.
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Information
Table of contents
- Cover
- Half Title
- Series Page
- Title Page
- Copyright Page
- Table of Contents
- Contributors
- Chapter 1 Enhancing Sentiment Analysis through Supervised Machine Learning Techniques
- Chapter 2 A Multimodal Sentiment Analysis Framework for Textual and Visual Cues
- Chapter 3 Multimodal Sentiment Analysis Applications in Healthcare: Enhancing Patient Care and Insights
- Chapter 4 Sentiment Analysis-Based Smart Support Assistant
- Chapter 5 Leveraging LSTM Networks for Predicting User Demand in the Fast-Moving Consumer Goods Market
- Chapter 6 Advancing Domain-Specific Adaptations of Large Language Models through Transfer Learning and Fine-Tuning Techniques: An Analytical Study
- Chapter 7 Sentiment Analysis of Social-Media Content on COVID-19 Vaccine
- Chapter 8 A Survey on Detection of Deepfake Text and Sentiment Analysis using Machine Learning Models
- Chapter 9 Exploring Emotions in Textual Data: Enhancing Analysis through POS Tagging and Visual Representation
- Chapter 10 A Comprehensive Review of Catastrophic Forgetting in Text Processing: Challenges, Mitigation Strategies, and Future Directions
- Chapter 11 Harnessing Emotion Detection in Healthcare: Techniques, Challenges, and Future Directions
- Chapter 12 EmotiVision: An Automated Deep Learning Framework for Sentiment Analysis through Facial Expression Recognition
- Name Index
- Subject Index