Deep Learning-Based Approaches for Sentiment Analysis (Algorithms for Intelligent Systems)

✍ Scribed by Agarwal

• Publisher: Springer
• Year: 2020
• Tongue: English
• Leaves: 326
• Category: Library

✦ Synopsis

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

Preface
Contents
About the Editors
Application of Deep Learning Approaches for Sentiment Analysis
1 Introduction
2 Taxonomy of Sentiment Analysis
2.1 Sentiment Analysis, Polarity, and Output
2.2 Levels of Sentiment Analysis
2.3 Domain Applicability, Training, and Testing Strategy
2.4 Language Support
2.5 Evaluation Measures
3 Text Representation for Sentiment Analysis
3.1 Embedded Vectors
3.2 Strategy of Initializing the Embedded Vectors
3.3 Enhancing the Embedded Vectors
3.4 Approximation Methods
3.5 Sampling-Based Approaches
3.6 Softmax-Based Approaches
4 Deep Learning Approaches for Sentiment Analysis
5 Evaluation Metrics for Sentiment Analysis
6 Benchmarked Datasets and Tools
7 Conclusion
References
Recent Trends and Advances in Deep Learning-Based Sentiment Analysis
1 Introduction
2 Related Work
3 Machine Learning Approaches for Sentiment Analysis
4 Study Rationale
5 Deep Learning Architectures
5.1 Convolutional Neural Networks
5.2 Recurrent Neural Networks
5.3 Bi-directional Recurrent Neural Network
6 Long Short-Term Memory (LSTMs)
7 Gated Recurrent Units (GRUs)
8 Attention Mechanism
9 Research Methodology
10 Approach to Sentiment Analysis Task Categorization
11 Coarse-Grain Sentiment Analysis
12 Fine-Grain Sentiment Analysis
13 Cross-Domain Sentiment Analysis
14 Conclusion and Survey Highlights
References
Deep Learning Adaptation with Word Embeddings for Sentiment Analysis on Online Course Reviews
1 Introduction
2 State of the Art
2.1 Sentiment Analysis in E-Learning Systems
2.2 Deep Learning for Sentiment Analysis
2.3 Word Embeddings for Sentiment Analysis
3 Word Embedding Representations for Text Mining
3.1 Word2Vec
3.2 GloVe
3.3 FastText
3.4 Intel
4 Deep Learning Components for Text Mining
4.1 Feed-Forward Neural Network (FNN)
4.2 Recurrent Neural Network (RNN)
4.3 Long Short-Term Memory (LSTM) Network
4.4 Convolutional Neural Network (CNN)
4.5 Normalization Layer (NL)
4.6 Attention Layer (AL)
4.7 Other Layers
5 Our Sentiment Predictor for E-Learning Reviews
5.1 Review Splitting
5.2 Word Embedding Modeling
5.3 Review Vectorization
5.4 Sentiment Model Definition
5.5 Sentiment Model Training and Prediction
6 Experimental Evaluation
6.1 Dataset
6.2 Baselines
6.3 Metrics
6.4 Deep Neural Network Model Regressor Performance
6.5 Contextual Word Embeddings Performance
7 Conclusions, Open Challenges, and Future Directions
References
Toxic Comment Detection in Online Discussions
1 Online Discussions and Toxic Comments
1.1 News Platforms and Other Online Discussions Forums
1.2 Classes of Toxicity
2 Deep Learning for Toxic Comment Classification
2.1 Comment Datasets for Supervised Learning
2.2 Neural Network Architectures
3 From Binary to Fine-Grained Classification
3.1 Why Is It a Hard Problem?
3.2 Transfer Learning
3.3 Explanations
4 Real-World Applications
4.1 Semi-automated Comment Moderation
4.2 Troll Detection
5 Current Limitations and Future Trends
5.1 Misclassification of Comments
5.2 Research Directions
6 Conclusions
References
Aspect-Based Sentiment Analysis of Financial Headlines and Microblogs
1 Introduction
2 Related Work
3 State-of-the-Art Models
3.1 ALA Model
3.2 IIIT Delhi Model
4 Our Methodology
4.1 Features
5 Aspect Classification Models
5.1 Models
5.2 Classification Model Training
6 Sentiment Models
6.1 Models
6.2 Sentiment Model Training
7 Evaluation
7.1 Data Set
7.2 Data Augmentation
7.3 Data Pre-processing
7.4 Metrics
7.5 Results
8 Conclusion and Future Work
References
Deep Learning-Based Frameworks for Aspect-Based Sentiment Analysis
1 Introduction
2 Problem Formulation
2.1 Aspect-Term Extraction
2.2 Aspect-Category Detection
3 Observation/Assumption in ABSA
4 Input Representation
5 Concepts Related to Deep Learning
5.1 Word-Embeddings
5.2 Long Short-Term Memory (LSTM)
5.3 Bi-directional Long Short-Term Memory (Bi-LSTM)
5.4 RNN with Attention
5.5 Convolution Neutral Network (CNN)
6 Deep Learning Architectures Used in ABSA
6.1 Sentiment Analysis
6.2 Aspect-Term Extraction
6.3 Aspect-Category Extraction
6.4 Aspect-Based Sentiment Detection
7 Conclusion
References
Transfer Learning for Detecting Hateful Sentiments in Code Switched Language
1 Introduction
1.1 Hate Speech Problem
1.2 Code Switched and Code Mixed Languages
1.3 Challenges in Code Switched and Code Mixed Languages
1.4 Deep Learning
1.5 Overview
2 Background and Related Work
2.1 Language Identification
2.2 POS Tagging
2.3 Named Entity Recognition
2.4 Sentiment Analysis
3 Dataset and Evaluation
3.1 HOT Dataset
3.2 Bohra et al. bohra2018dataset dataset
3.3 HEOT Dataset
3.4 Davidson Dataset
4 Methodology
4.1 SVM and Random Forest
4.2 Ternary Trans-CNN Model
4.3 LSTM-Based Model
4.4 MIMCT Model
5 Results
5.1 SVM and Random Forest Classifier
5.2 Ternary Trans-CNN Model
5.3 LSTM Model with Transfer Learning
5.4 MIMCT Model
6 Conclusion
7 Future Work
References
Multilingual Sentiment Analysis
1 Introduction
1.1 Low Resource Language
1.2 Challenges of Sentiment Analysis
1.3 Deep Learning
2 Literature Survey
2.1 High Resource Languages
2.2 Lexicon-Based Approaches
2.3 Traditional Machine Learning-Based Approaches
2.4 Low Resource Languages
3 Word Embeddings for Sentiment Analysis
3.1 Refining Word Embeddings for Sentiment Analysis
3.2 Improving Word Embedding Coverage in Low Resource Languages
4 Deep Learning Techniques for Multilingual Sentiment Analysis
4.1 Convolutional Neural Networks
4.2 Recurrent Neural Networks
4.3 Autoencoders
4.4 Bilingual Constrained Recursive Autoencoders
4.5 AROMA
4.6 Siamese Neural Networks
5 Discussion
6 Conclusion
References
Sarcasm Detection Using Deep Learning-Based Techniques
1 Introduction
2 Related Work
3 Grice’s Maxims
4 Challenges in Sarcasm Detection
5 Dataset Description
6 Feature Description
7 Process Outline
8 Models Used
9 Experiments and Results
10 Future Scope
References
Deep Learning Approaches for Speech Emotion Recognition
1 Introduction
2 Feature Extraction
3 Feature Selection
4 Classical Approaches
4.1 Speaker-Dependent SER
4.2 Speaker-Independent SER
4.3 Other Models
5 Deep Learning Approaches
6 System Overview
6.1 Classical Approach for SER
6.2 Deep Learning Approaches for SER
6.3 Critical Comparision
7 Evaluation
7.1 Dataset Description
7.2 Original Results
7.3 Results Obtained
8 Comparison of Existing Approaches
9 Conclusions
References
Bidirectional Long Short-Term Memory-Based Spatio-Temporal in Community Question Answering
1 Introduction
2 Related Works
3 Methodology
3.1 Preprocessing Steps
3.2 Best Answer Prediction
4 Experimental Setup: Answer Classification
5 Experiment II: Answer Ranking
6 Conclusion
References
Comparing Deep Neural Networks to Traditional Models for Sentiment Analysis in Turkish Language
1 Introduction
2 Methodology
3 Experimental Setup and Results
3.1 Dataset
3.2 Traditional BOW Approach
3.3 Deep Learning Architecture
4 Conclusion
References