Sentiment Analysis (using LSTMs)

This course is a comprehensive program designed for individuals eager to explore the intersection of natural language processing (NLP) and deep learning. In this project-based course, participants will delve into the world of sentiment analysis, focusing on movie reviews. 

The course will equip students with the knowledge and hands-on experience needed to build advanced sentiment analysis models using Long Short-Term Memory (LSTM) networks, a type of deep learning architecture. By the end of the course, participants will have the skills to develop sophisticated models capable of discerning the sentiment expressed in movie reviews, from positive and negative to nuanced emotions.

Learning Outcomes:

Upon completing this course, participants will:

- Develop a deep understanding of sentiment analysis and its real-world applications.

- Acquire proficiency in Python programming for NLP and deep learning using LSTMs.

- Master techniques for data preprocessing, tokenization, and sequence padding.

- Learn how to design, build, and train LSTM-based sentiment analysis models.

- Gain expertise in hyperparameter tuning and model optimization.

- Evaluate model performance using industry-standard metrics.

- Apply advanced NLP concepts to analyze sentiment nuances in movie reviews.

- Deploy deep learning models for sentiment analysis in practical applications.

Prerequisites:

- Solid programming skills in Python.

- Basic knowledge of machine learning concepts and NLP fundamentals.

- Access to a Python development environment with relevant libraries for deep learning, such as TensorFlow or PyTorch.

Libraries and Programming Language Used:

- Python for coding and scripting.

- TensorFlow or PyTorch for building and training deep learning models.

- Common NLP libraries like spaCy for text preprocessing.

Course Syllabus:

Introduction to Sentiment Analysis

   - Understanding sentiment analysis and its significance.

   - Types of sentiment classification and use cases.

Setting Up the Development Environment

   - Installing and configuring Python, TensorFlow/PyTorch, and relevant libraries.

   - Preparing the development environment for deep learning projects.

Data Acquisition and Preprocessing

   - Gathering movie review datasets.

   - Cleaning and preprocessing text data for LSTM model input.

Understanding LSTMs

   - Introduction to Long Short-Term Memory networks.

   - How LSTMs handle sequence data.

Tokenization and Sequence Padding

   - Tokenizing text data into sequences.

   - Padding sequences for input to LSTM networks.

Building LSTM-Based Sentiment Analysis Models

   - Designing and implementing LSTM architectures for sentiment classification.

   - Training LSTM models on movie review datasets.

Model Evaluation and Metrics

   - Assessing model performance using metrics like accuracy, precision, recall, and F1-score.

Hyperparameter Tuning and Optimization

   - Strategies for optimizing LSTM models.

   - Hyperparameter tuning to improve sentiment analysis accuracy.