Deep learning-based approaches for sentiment analysis
Author(s)
Bibliographic Information
Deep learning-based approaches for sentiment analysis
（Algorithms for intelligent systems）
Springer, c2020
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Note
Includes bibliographical references
Description and Table of Contents
Description
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
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.
by "Nielsen BookData"
