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.
- 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
Sprache
Verlagsort
Verlagsgruppe
Elsevier Science & Techn.
Dateigröße
ISBN-13
978-0-443-22010-4 (9780443220104)
Schweitzer Klassifikation
1. Role of Machine Learning in Sentiment Analysis: Trends, Challenges and Future Directions Vidyasagar Shetty and Shabari Shedthi 2. A Comparative analysis of Machine Learning and Deep Learning Techniques for Aspect-based Sentiment Analysis Getzi Jeba Leelipushpam Paulraj, Theresa V. Cherian, Joyce Beryl Princess and Immanuel Johnraja Jebadurai 3. A systematic survey on Text-based Dimensional Sentiment Analysis: Advancements, Challenges and Future directions Saroj Date and Sahin Deshmukh 4. A model of time in Natural Linguistic Reasoning Daniela López De Luise and Sebastian Cippitelli 5. Hate speech detection using LSTM network from Twitter speech data Ravi Shekhar Tiwari 6. Enhanced Performance of Drug Review Classification from Social Network by ADASYN Training and NLP Techniques P.M. Lavanya and E Sasikala 7. Emotion Detection from Text Data using Machine Learning for Human Behavior Analysis Muskan Garg and Chandni Saxena 8. Optimization of Effectual Sentiment Analysis in Film Reviews using Machine Learning Techniques S. Balamurugan 9. Deep Learning for Double Negative Detection in Text Data for Customer Feedback Analysis on a Product Deepika Ghai, Suman Lata Tripathi, Ramandeep Sandhu, Ranjit Kaur, Mohammad Faiz and Gurleen Kaur Walia 10. Sarcasm Detection using Deep Learning in Natural Language Processing Santhanam Lakshmi 11. Abusive comment detection in Tamil Language using Deep Learning Vedika Gupta, Deepawali Sharma and Vivek Kumar Singh 12. Implementation of Sentiment Analysis in Stock Market Prediction using variants of GARCH models Vijayalakshmi V 13. A Metaheuristic Harmony Search Optimization Based Approach for Hateful and Offensive Speech Detection in social media S. Saroja and S Haseena
