Sentiment Analysis

6.

Deep Learning for Sentiment Analysis

6.1.

Neural Network Fundamentals

6.1.1.

Perceptrons and Multi-layer Perceptrons

6.1.2.

Activation Functions

6.1.3.

6.1.4.

Optimization Algorithms

6.1.5.

Regularization Techniques

6.2.

Recurrent Neural Networks

6.2.1.

6.2.1.1.

6.2.1.2.

Vanishing Gradient Problem

6.2.2.

Long Short-Term Memory

6.2.2.1.

LSTM Architecture

6.2.2.2.

6.2.2.3.

6.2.2.4.

6.2.2.5.

Bidirectional LSTM

6.2.2.6.

6.2.3.

Gated Recurrent Units

6.2.3.1.

GRU Architecture

6.2.3.2.

6.2.3.3.

6.2.3.4.

Comparison with LSTM

6.3.

Convolutional Neural Networks for Text

6.3.1.

1D Convolutions

6.3.2.

Multiple Filter Sizes

6.3.3.

Pooling Operations

6.3.4.

Multi-channel CNNs

6.3.5.

CNN-LSTM Combinations

6.4.

Attention Mechanisms

6.4.1.

Attention Concept

6.4.2.

6.4.3.

Multi-Head Attention

6.4.4.

Attention Visualization

6.5.

Transformer Architecture

6.5.1.

Encoder-Decoder Structure

6.5.2.

Position Encoding

6.5.3.

Layer Normalization

6.5.4.

Feed-Forward Networks

6.6.

Pre-trained Language Models

6.6.1.

Transfer Learning Principles

6.6.2.

Fine-tuning Strategies

6.6.3.

Domain Adaptation

6.6.4.

Few-Shot Learning

6.7.

Advanced Architectures

6.7.1.

Hierarchical Attention Networks

6.7.2.

Memory Networks

6.7.3.

Graph Neural Networks

6.7.4.

Capsule Networks

6.8.

Training Considerations

6.8.1.

Data Augmentation

6.8.2.

Batch Processing

6.8.3.

Learning Rate Scheduling

6.8.4.

6.8.5.

Model Checkpointing

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7. Aspect-Based Sentiment Analysis