Sentiment Analysis and Emotion Recognition
( 24 Modules )

Module #1
Introduction to Sentiment Analysis
Overview of Sentiment Analysis, its applications, and importance

Module #2
Emotion Recognition Fundamentals
Understanding emotions, emotional intelligence, and recognition techniques

Module #3
Types of Sentiment Analysis
Exploring binary, multi-class, and aspect-based sentiment analysis

Module #4
Emotion Recognition Techniques
Rule-based, machine learning, and deep learning approaches for emotion recognition

Module #5
Text Preprocessing for Sentiment Analysis
Tokenization, stemming, lemmatization, and stopword removal for text preprocessing

Module #6
Feature Extraction for Sentiment Analysis
Extraction of meaningful features from text data for sentiment analysis

Module #7
Supervised Learning for Sentiment Analysis
Training machine learning models for sentiment analysis using labeled data

Module #8
Unsupervised Learning for Sentiment Analysis
Clustering and dimensionality reduction techniques for sentiment analysis

Module #9
Deep Learning for Sentiment Analysis
Using convolutional and recurrent neural networks for sentiment analysis

Module #10
Emotion Recognition using Facial Expressions
Analyzing facial expressions to recognize emotions using computer vision

Module #11
Emotion Recognition using Speech Signals
Analyzing speech signals to recognize emotions using audio processing

Module #12
Emotion Recognition using Physiological Signals
Analyzing physiological signals (e.g., EEG, ECG) to recognize emotions

Module #13
Sentiment Analysis for Social Media
Challenges and techniques for sentiment analysis in social media text data

Module #14
Sentiment Analysis for Product Reviews
Extracting insights from product reviews using sentiment analysis

Module #15
Emotion Recognition in Human-Computer Interaction
Designing affective computing systems for human-computer interaction

Module #16
Evaluation Metrics for Sentiment Analysis
Measuring performance of sentiment analysis models using accuracy, F1-score, etc.

Module #17
Challenges and Limitations of Sentiment Analysis
Dealing with noisy data, sarcasm, and cultural differences in sentiment analysis

Module #18
Real-World Applications of Sentiment Analysis
Use cases in customer service, marketing, and healthcare

Module #19
Real-World Applications of Emotion Recognition
Use cases in gaming, education, and affective computing

Module #20
Building a Sentiment Analysis System
Hands-on exercise to build a simple sentiment analysis system

Module #21
Building an Emotion Recognition System
Hands-on exercise to build a simple emotion recognition system

Module #22
Advanced Topics in Sentiment Analysis
Aspect-based sentiment analysis, sentiment analysis for low-resource languages

Module #23
Advanced Topics in Emotion Recognition
Multimodal emotion recognition, emotion recognition for mental health

Module #24
Course Wrap-Up & Conclusion
Planning next steps in Sentiment Analysis and Emotion Recognition career

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Sentiment Analysis and Emotion Recognition ( 24 Modules )

Sentiment Analysis and Emotion Recognition
( 24 Modules )