Computational Intelligence Methods for Sentiment Analysis in Natural Language Processing Applications

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