Table of contents : 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