About the Book:
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.
Table of Contents
Application of Deep Learning Approaches for Sentiment AnalysisRecent Trends and Advances in Deep Learning-Based Sentiment Analysis Deep Learning Adaptation with Word Embeddings for Sentiment Analysis on Online Course ReviewsToxic Comment Detection in Online DiscussionsAspect-Based Sentiment Analysis of Financial Headlines and MicroblogsDeep Learning-Based Frameworks for Aspect-Based Sentiment Analysis Deep Learning-Based Frameworks for Aspect-Based Sentiment Analysis
Transfer Learning for Detecting Hateful Sentiments in Code Switched LanguageTransfer Learning for Detecting Hateful Sentiments in Code Switched Language
Multilingual Sentiment AnalysisSarcasm Detection Using Deep Learning-Based TechniquesDeep Learning Approaches for Speech Emotion RecognitionBidirectional Long Short-Term Memory-Based Spatio-Temporal in Community Question AnsweringComparing Deep Neural Networks to Traditional Models for Sentiment Analysis in Turkish Language
