NLP for Sentiment Analysis
( 25 Modules )

Module #1
Introduction to Sentiment Analysis
Overview of sentiment analysis, its importance, and applications in industry and research

Module #2
NLP Fundamentals
Basics of natural language processing, including text preprocessing, tokenization, and feature extraction

Module #3
Types of Sentiment Analysis
Exploration of different types of sentiment analysis, including binary, multi-class, and aspect-based sentiment analysis

Module #4
Text Preprocessing for Sentiment Analysis
Techniques for preprocessing text data for sentiment analysis, including tokenization, stemming, and lemmatization

Module #5
Stop Words and Removing Noise
Importance of stop words and noise removal in sentiment analysis, and techniques for doing so

Module #6
Feature Extraction for Sentiment Analysis
Methods for extracting relevant features from text data, including bag-of-words, TF-IDF, and word embeddings

Module #7
Word Embeddings for Sentiment Analysis
In-depth exploration of word embeddings, including Word2Vec and GloVe, and their applications in sentiment analysis

Module #8
Traditional Machine Learning for Sentiment Analysis
Overview of traditional machine learning algorithms for sentiment analysis, including Naive Bayes and Support Vector Machines

Module #9
Deep Learning for Sentiment Analysis
Introduction to deep learning architectures for sentiment analysis, including convolutional and recurrent neural networks

Module #10
Convolutional Neural Networks (CNNs) for Sentiment Analysis
In-depth exploration of CNNs for sentiment analysis, including text classification and aspect extraction

Module #11
Recurrent Neural Networks (RNNs) for Sentiment Analysis
In-depth exploration of RNNs for sentiment analysis, including long short-term memory (LSTM) and gated recurrent unit (GRU) networks

Module #12
Attention Mechanisms for Sentiment Analysis
Overview of attention mechanisms and their applications in sentiment analysis, including aspect extraction and sentiment intensity analysis

Module #13
Sentiment Analysis with Transformers
Introduction to transformer-based architectures for sentiment analysis, including BERT and RoBERTa

Module #14
Aspect-Based Sentiment Analysis
Overview of aspect-based sentiment analysis, including aspect extraction and sentiment intensity analysis

Module #15
Aspect Extraction Techniques
In-depth exploration of aspect extraction techniques, including rule-based, machine learning, and deep learning approaches

Module #16
Sentiment Analysis with Imbalanced Data
Techniques for handling imbalanced datasets in sentiment analysis, including oversampling, undersampling, and class weighting

Module #17
Evaluation Metrics for Sentiment Analysis
Overview of evaluation metrics for sentiment analysis, including accuracy, precision, recall, and F1-score

Module #18
Handling Out-of-Vocabulary (OOV) Words
Techniques for handling OOV words in sentiment analysis, including subwording and character-level modeling

Module #19
Handling Sarcasm and Irony
Challenges and techniques for handling sarcasm and irony in sentiment analysis

Module #20
domain Adaptation for Sentiment Analysis
Overview of domain adaptation techniques for sentiment analysis, including transfer learning and multi-task learning

Module #21
Sentiment Analysis for Low-Resource Languages
Challenges and techniques for sentiment analysis in low-resource languages, including machine translation and transfer learning

Module #22
Case Study:Sentiment Analysis in Social Media
Real-world application of sentiment analysis in social media, including data preprocessing, feature extraction, and model evaluation

Module #23
Case Study:Sentiment Analysis in Product Reviews
Real-world application of sentiment analysis in product reviews, including aspect extraction and sentiment intensity analysis

Module #24
Advanced Topics in Sentiment Analysis
Exploration of advanced topics in sentiment analysis, including multimodal sentiment analysis and adversarial sentiment analysis

Module #25
Course Wrap-Up & Conclusion
Planning next steps in NLP for Sentiment Analysis career

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NLP for Sentiment Analysis ( 25 Modules )

NLP for Sentiment Analysis
( 25 Modules )