Sentiment Analysis has become increasingly important in recent years for nearly all online applications. Sentiment Analysis depends heavily on Artificial Intelligence (AI) technology wherein computational intelligence approaches aid in deriving the opinions/emotions of human beings. 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. The applications of Sentiment Analysis are enormous, ranging from business to biomedical and clinical applications. However, the combination of AI methods and Sentiment Analysis is one of the rarest commodities in the literature. The literatures either gives more importance to the application alone or to the AI/CI methodology.
Computational 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 authors provide readers with an in-depth look at the challenges and solutions associated with the different types of Sentiment Analysis, including case studies and real-world scenarios from across the globe. Development of scientific and enterprise applications are covered, which will aid computer scientists in building practical/real-world AI-based Sentiment Analysis systems.
1. Computational Intelligence Methods for Sentiment Analysis in Natural Language Processing Applications - p. i 2. Cover - p. i 3. Title Page - p. iii 4. Copyright Page - p. iv 5. Table of Contents - p. iv 6. List of contributors - p. xi 7. Preface - p. xv 8. 1 Role of machine learning in sentiment analysis: trends, challenges, and future directions - p. 1 9. 2 A comparative analysis of machine learning and deep learning techniques for aspect-based sentiment analysis - p. 23 10. 3 A systematic survey on text-based dimensional sentiment analysis: advancements, challenges, and future directions - p. 39 11. 4 A model of time in natural linguistic reasoning - p. 59 12. 5 Hate speech detection using LSTM and explanation by LIME (local interpretable model-agnostic explanations) - p. 93 13. 6 Enhanced performance of drug review classification from social networks by improved ADASYN training and Natural Language Processing techniques - p. 111 14. 7 Emotion detection from text data using machine learning for human behavior analysis - p. 129 15. 8 Optimization of effectual sentiment analysis in film reviews using machine learning techniques - p. 145 16. 9 Deep learning for double-negative detection in text data for customer feedback analysis on a product - p. 165 17. 10 Sarcasm detection using deep learning in natural language processing - p. 187 18. 11 Abusive comment detection in Tamil using deep learning - p. 207 19. 12 Implementation of sentiment analysis in stock market prediction using variants of GARCH models - p. 227 20. 13 A metaheuristic harmony search optimization–based approach for hateful and offensive speech detection in social media - p. 251 21. Index - p. 265
Sentiment Analysis has become increasingly important in recent years for nearly all online applications. Sentiment Analysis depends heavily on Artificial Intelligence (AI) technology wherein computational intelligence approaches aid in deriving the opinions/emotions of human beings. 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. The applications of Sentiment Analysis are enormous, ranging from business to biomedical and clinical applications. However, the combination of AI methods and Sentiment Analysis is one of the rarest commodities in the literature. The literatures either gives more importance to the application alone or to the AI/CI methodology.
Computational 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 authors provide readers with an in-depth look at the challenges and solutions associated with the different types of Sentiment Analysis, including case studies and real-world scenarios from across the globe. Development of scientific and enterprise applications are covered, which will aid computer scientists in building practical/real-world AI-based Sentiment Analysis systems.
1. Computational Intelligence Methods for Sentiment Analysis in Natural Language Processing Applications - p. i 2. Cover - p. i 3. Title Page - p. iii 4. Copyright Page - p. iv 5. Table of Contents - p. iv 6. List of contributors - p. xi 7. Preface - p. xv 8. 1 Role of machine learning in sentiment analysis: trends, challenges, and future directions - p. 1 9. 2 A comparative analysis of machine learning and deep learning techniques for aspect-based sentiment analysis - p. 23 10. 3 A systematic survey on text-based dimensional sentiment analysis: advancements, challenges, and future directions - p. 39 11. 4 A model of time in natural linguistic reasoning - p. 59 12. 5 Hate speech detection using LSTM and explanation by LIME (local interpretable model-agnostic explanations) - p. 93 13. 6 Enhanced performance of drug review classification from social networks by improved ADASYN training and Natural Language Processing techniques - p. 111 14. 7 Emotion detection from text data using machine learning for human behavior analysis - p. 129 15. 8 Optimization of effectual sentiment analysis in film reviews using machine learning techniques - p. 145 16. 9 Deep learning for double-negative detection in text data for customer feedback analysis on a product - p. 165 17. 10 Sarcasm detection using deep learning in natural language processing - p. 187 18. 11 Abusive comment detection in Tamil using deep learning - p. 207 19. 12 Implementation of sentiment analysis in stock market prediction using variants of GARCH models - p. 227 20. 13 A metaheuristic harmony search optimization–based approach for hateful and offensive speech detection in social media - p. 251 21. Index - p. 265
