Deep Learning-Based Approaches for Sentiment Analysis
Deep Learning-Based Approaches for Sentiment Analysis
edited by Basant Agarwal, Richi Nayak, Namita Mittal, Srikanta Patnaik
- Primera edición 2020
- 1 recurso en línea (XII, 319 páginas)
- Algorithms for Intelligent Systems 2524-7565 Engineering (Springer-11647) .
Chapter 1. Application of Deep Learning Approaches for Sentiment Analysis: A Survey -- Chapter 2. Recent Trends and Advances in Deep Learning based Sentiment Analysis -- Chapter 3. - Deep Learning Adaptation with Word Embeddings for Sentiment Analysis on Online Course Reviews -- Chapter 4. Toxic Comment Detection in Online Discussions -- Chapter 5. Aspect Based Sentiment Analysis of Financial Headlines and Microblogs -- Chapter 6. Deep Learning based frameworks for Aspect Based Sentiment Analysis -- Chapter 7. Transfer Learning for Detecting Hateful Sentiments in Code Switched Language -- Chapter 8. Multilingual Sentiment Analysis -- Chapter 9. Sarcasm Detection using deep learning -- Chapter 10. Deep Learning Approaches for Speech Emotion Recognition -- Chapter 11. Bidirectional Long Short Term Memory Based Spatio-Temporal In Community Question Answering -- Chapter 12. Comparing Deep Neural Networks to Traditional Models for Sentiment Analysis in Turkish Language.
This book covers deep-learning-based approaches for sentiment analysis, a relatively new, but fast-growing research area, which has significantly changed in the past few years. The book presents a collection of state-of-the-art approaches, focusing on the best-performing, cutting-edge solutions for the most common and difficult challenges faced in sentiment analysis research. Providing detailed explanations of the methodologies, the book is a valuable resource for researchers as well as newcomers to the field. .
9789811512162
10.1007/978-981-15-1216-2 doi
Proceso en lenguaje natural (Informática)
QA76.9 .N38 / 2020 EB
Chapter 1. Application of Deep Learning Approaches for Sentiment Analysis: A Survey -- Chapter 2. Recent Trends and Advances in Deep Learning based Sentiment Analysis -- Chapter 3. - Deep Learning Adaptation with Word Embeddings for Sentiment Analysis on Online Course Reviews -- Chapter 4. Toxic Comment Detection in Online Discussions -- Chapter 5. Aspect Based Sentiment Analysis of Financial Headlines and Microblogs -- Chapter 6. Deep Learning based frameworks for Aspect Based Sentiment Analysis -- Chapter 7. Transfer Learning for Detecting Hateful Sentiments in Code Switched Language -- Chapter 8. Multilingual Sentiment Analysis -- Chapter 9. Sarcasm Detection using deep learning -- Chapter 10. Deep Learning Approaches for Speech Emotion Recognition -- Chapter 11. Bidirectional Long Short Term Memory Based Spatio-Temporal In Community Question Answering -- Chapter 12. Comparing Deep Neural Networks to Traditional Models for Sentiment Analysis in Turkish Language.
This book covers deep-learning-based approaches for sentiment analysis, a relatively new, but fast-growing research area, which has significantly changed in the past few years. The book presents a collection of state-of-the-art approaches, focusing on the best-performing, cutting-edge solutions for the most common and difficult challenges faced in sentiment analysis research. Providing detailed explanations of the methodologies, the book is a valuable resource for researchers as well as newcomers to the field. .
9789811512162
10.1007/978-981-15-1216-2 doi
Proceso en lenguaje natural (Informática)
QA76.9 .N38 / 2020 EB