Computational Intelligence Methods for Sentiment Analysis in Natural Language Processing Applications
- 1st Edition - January 19, 2024
- Imprint: Morgan Kaufmann
- Editor: D. Jude Hemanth
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 2 0 0 9 - 8
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 2 0 1 0 - 4
Computational Intelligence for Sentiment Analysis in Natural Language Processing Applications provides a solution to this problem through detailed technical coverage of AI-bas… Read more
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Request a sales quoteComputational Intelligence for Sentiment Analysis in Natural Language Processing Applications provides a solution to this problem through detailed technical coverage of AI-based Sentiment Analysis methods for various applications. The book's authors provide readers with an in-depth look at the challenges and associated solutions, including case studies and real-world scenarios from across the globe. Development of scientific and enterprise applications are covered that will aid computer scientists in building practical/real-world AI-based Sentiment Analysis systems. With the vast increase in Big Data, computational intelligence approaches have become a necessity for Natural Language Processing and Sentiment Analysis in a wide range of decision-making application areas.
- Includes basic concepts, technical explanations, and case studies for in-depth explanation of the Sentiment Analysis
- Aids computer scientists in developing practical/real-world AI-based Sentiment Analysis systems
- Provides readers with real-world development applications of AI-based Sentiment Analysis, including transfer learning for opinion mining from pandemic medical data, sarcasm detection using neural networks in human-computer interaction, and emotion detection using the random-forest algorithm
Computer Scientists and researchers in Computational Intelligence and Machine Learning, specifically in the field of developing Natural Language Processing algorithms and applications. As such, academics, researchers, and professionals in a variety of research fields who work with AI, algorithms, and Machine Learning and their applications to Sentiment Analysis will be a target audience. Linguistics researchers, social science researchers, management science researchers, government and non-government organizations, data analysts, and engineers working on NLP and Sentiment Analysis
- Cover image
- Title page
- Table of Contents
- Copyright
- List of contributors
- Preface
- 1. Role of machine learning in sentiment analysis: trends, challenges, and future directions
- Abstract
- 1.1 Introduction
- 1.2 Related background
- 1.3 Performance metrics
- 1.4 Tools for sentiment analysis
- 1.5 Trends of sentiment analysis
- 1.6 Challenges
- 1.7 Conclusion
- 1.8 Future direction
- References
- 2. A comparative analysis of machine learning and deep learning techniques for aspect-based sentiment analysis
- Abstract
- 2.1 Introduction
- 2.2 Steps in sentiment analysis
- 2.3 Applications of sentiment analysis
- 2.4 Types of sentiment analysis
- 2.5 Aspect-based sentiment analysis
- 2.6 Performance metrics
- 2.7 Datasets
- 2.8 Future research challenges
- 2.9 Conclusion
- References
- 3. A systematic survey on text-based dimensional sentiment analysis: advancements, challenges, and future directions
- Abstract
- 3.1 Introduction
- 3.2 Literature survey
- 3.3 Observations drawn from the literature survey
- 3.4 Open issues and challenges in dimensional sentiment analysis
- 3.5 Future directions
- 3.6 Conclusion
- References
- 4. A model of time in natural linguistic reasoning
- Abstract
- 4.1 Introduction
- 4.2 Human biology of time
- 4.3 Evidence of timelines in the brain: time in linguistic reasoning
- 4.4 Some clues and tests
- 4.5 Conclusions and future work
- References
- 5. Hate speech detection using LSTM and explanation by LIME (local interpretable model-agnostic explanations)
- Abstract
- 5.1 Introduction
- 5.2 Bag of words
- 5.3 Term frequency–inverse document frequency
- 5.4 Glove—word embedding
- 5.5 Long short-term memory
- 5.6 LIME—local interpretable model–agnostic explanations
- 5.7 Code
- References
- 6. Enhanced performance of drug review classification from social networks by improved ADASYN training and Natural Language Processing techniques
- Abstract
- 6.1 Introduction
- 6.2 Related works
- 6.3 Proposed model
- 6.4 Results and discussion
- 6.5 Conclusion
- References
- 7. Emotion detection from text data using machine learning for human behavior analysis
- Abstract
- 7.1 Introduction
- 7.2 Available tools and resources
- 7.3 Methods and materials
- 7.4 Outlook
- 7.5 Conclusion
- References
- 8. Optimization of effectual sentiment analysis in film reviews using machine learning techniques
- Abstract
- 8.1 Introduction
- 8.2 Literature Survey
- 8.3 Proposed System
- 8.4 Computational Experiments and Result Analysis
- 8.5 Conclusion
- References
- 9. Deep learning for double-negative detection in text data for customer feedback analysis on a product
- Abstract
- 9.1 Introduction
- 9.2 Related work
- 9.3 Proposed methodology
- 9.4 Experimental results and discussion
- 9.5 Conclusion
- References
- 10. Sarcasm detection using deep learning in natural language processing
- Abstract
- 10.1 Introduction
- 10.2 Datasets
- 10.3 Overall process of sarcasm detection
- 10.4 Sarcasm detection and classification
- 10.5 Sarcasm detection: python code implementation
- 10.6 Evaluation
- 10.7 Results and discussion
- 10.8 Conclusion
- References
- Further reading
- 11. Abusive comment detection in Tamil using deep learning
- Abstract
- 11.1 Introduction
- 11.2 Related work
- 11.3 Dataset description
- 11.4 Methodology
- 11.5 Results
- 11.6 Conclusion
- References
- 12. Implementation of sentiment analysis in stock market prediction using variants of GARCH models
- Abstract
- 12.1 Introduction
- 12.2 Literature review
- 12.3 Methodology
- 12.4 Sentiment analysis on twitter data
- 12.5 Forecasting on financial stock data
- 12.6 Implementation of GARCH models
- 12.7 Stimulating stock prices
- 12.8 Conclusion
- References
- 13. A metaheuristic harmony search optimization–based approach for hateful and offensive speech detection in social media
- Abstract
- 13.1 Introduction
- 13.2 Literature survey
- 13.3 Methodology
- 13.4 Experiments and results
- 13.5 Conclusion
- References
- Index
- Edition: 1
- Published: January 19, 2024
- Imprint: Morgan Kaufmann
- No. of pages: 294
- Language: English
- Paperback ISBN: 9780443220098
- eBook ISBN: 9780443220104
DH
D. Jude Hemanth
Dr. D. Jude Hemanth is currently working as a professor in Department of ECE, Karunya University, Coimbatore, India. He also holds the position of “Visiting Professor” in Faculty of Electrical Engineering and Information Technology, University of Oradea, Romania. He also serves as the “Research Scientist” of Computational Intelligence and Information Systems (CI2S) Lab, Argentina; LAPISCO research lab, Brazil; RIADI Lab, Tunisia; Research Centre for Applied Intelligence, University of Craiova, Romania and e-health and telemedicine group, University of Valladolid, Spain.
Dr. Hemanth received his B.E degree in ECE from Bharathiar University in 2002, M.E degree in communication systems from Anna University in 2006 and Ph.D. from Karunya University in 2013. He has published 37 edited books with reputed publishers such as Elsevier, Springer and IET. His research areas include Computational Intelligence and Image processing. He has authored more than 200 research papers in reputed SCIE indexed International Journals and Scopus indexed International Conferences.
Affiliations and expertise
Professor, ECE Department, Karunya Institute of Technology and Sciences, Coimbatore, IndiaRead Computational Intelligence Methods for Sentiment Analysis in Natural Language Processing Applications on ScienceDirect