Computational Intelligence Methods for Sentiment Analysis in Natural Language Processing Applications

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 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