Sentiment Analysis Unveiled

Techniques, Applications, and Innovations
1. Auflage 2025
ISBN: 978-1-032-82495-6
Verlag: Taylor & Francis Ltd (Sales)

Buch, Englisch, 178 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 431 g

Reihe: Edge AI in Future Computing

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.

Zielgruppe

Postgraduate, Professional Practice & Development, and Professional Reference

Autoren/Hrsg.

Nandal, Neha

Tanwar, Rohit

Sapra, Varun

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Mathematik | Informatik EDV | Informatik Technische Informatik

Inhaltsverzeichnis

1. Enhancing Sentiment Analysis through Supervised Machine Learning Techniques. 2. A Multimodal Sentiment Analysis Framework for Textual and Visual Cues. 3. Multimodal Sentiment Analysis Applications in Healthcare: Enhancing Patient Care and Insights. 4. Sentiment Analysis-Based Smart Support Assistant. 5. Leveraging LSTM Networks for Predicting User Demand in the Fast-Moving Consumer Goods Market. 6. Advancing Domain-Specific Adaptations of Large Language Models through Transfer Learning and Fine-Tuning Techniques: An Analytical Study. 7. Sentiment Analysis of Social-Media Content on COVID-19 Vaccine. 8. A Survey on Detection of Deepfake Text and Sentiment Analysis using Machine Learning Models. 9. Exploring Emotions in Textual Data: Enhancing Analysis through POS Tagging and Visual Representation. 10. A Comprehensive Review of Catastrophic Forgetting in Text Processing: Challenges, Mitigation Strategies, and Future Directions. 11. Harnessing Emotion Detection in Healthcare: Techniques, Challenges, and Future Directions 12. EmotiVision: An Automated Deep Learning Framework for Sentiment Analysis through Facial Expression Recognition

Über Autor(innen)

Neha Nandal is an Associate Professor in Computer Science, specializing in Artificial Intelligence, Machine Learning. With over 9 years of academic and research experience, she currently serves as an Associate Professor at Geethanjali College of Engineering and Technology, Hyderabad, India.

She has an impressive publication record, with over 15 research papers published in prestigious SCI and SCOPUS-indexed journals, as well as 15 conference presentations at national and international venues. In recognition of her innovative work, she has also published 4 patents related to AI and machine learning applications in secure computing.

Her scholarly achievements extend to authorship, with a recently published book on ADLMHMS-2020: Application of Deep Learning Methods in Healthcare and Medical Science which serves as a resource for students, researchers, and industry professionals alike.

Rohit Tanwar received his bachelor’s degree and PhD degree in Computer Science Engineering from Kurukshetra University. He received his master’s degree from the YMCA University of Science and Technology. He has over 10 years of experience in teaching and is currently an Associate Professor in the School of Computer Science at UPES, Dehradun. His areas of research include network security, optimization techniques, human computing, soft computing, cloud computing, and data mining. He also supervises PhD research scholars in the fields of network security, automatic target recognition, and healthcare.

Varun Sapra is presently at the School of Computer Science at the University of Petroleum and Energy Studies. He received his PhD in Computer Science and Engineering from Jagannath University. He has 17 years of combined experience in both industry and academia. Before joining academics, he was in the corporate sector and worked in companies like Cupid Software, WebOpac Applications, and CMA. His research areas include machine learning, decision support systems, case-based reasoning, and self-organizing maps.