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Natural language processing (NLP)




Python, Some familiarity with NLP concepts

Skills to be Learned:

Building and training models for sentiment classification.

Sentiment Analysis (using Machine Learning)

Gain expertise in sentiment analysis with machine learning through our hands-on course. Learn to classify movie reviews into emotions using Python, NLP tools, and practical datasets.

In this project-based course, participants will explore how to automatically determine the sentiment expressed in movie reviews using machine learning techniques and NLP tools. Students will engage in practical hands-on exercises, working with real movie review datasets, and learn to build sentiment analysis models that can classify text as positive, negative, or neutral. By the end of the course, participants will have the skills to develop sentiment analysis solutions and gain valuable insights into the sentiments of moviegoers.

Learning Outcomes:

Upon completing this course, participants will:

- Understand the fundamentals of sentiment analysis and its applications.

- Gain hands-on experience with Python programming for NLP and machine learning.

- Learn how to acquire, preprocess, and analyze textual data for sentiment analysis tasks.

- Develop sentiment analysis models that can classify movie reviews into different sentiment categories.

- Evaluate the performance of sentiment analysis models using appropriate metrics.

- Apply sentiment analysis techniques to real-world movie review datasets.


- Basic programming skills in Python.

- Familiarity with fundamental NLP concepts, though not mandatory, will be helpful.

- Access to a Python environment with the required libraries for NLP and machine learning.

Libraries and Programming Language Used:

- Python for coding and scripting.

- Common NLP libraries such as NLTK or spaCy for text processing.

- Machine learning frameworks like scikit-learn for building sentiment analysis models.

Course Syllabus:

Introduction to Sentiment Analysis

   - Understanding the importance and applications of sentiment analysis.

   - Overview of sentiment classification (positive, negative, neutral).

Setting Up the Development Environment

   - Installing and configuring the necessary Python libraries.

   - Preparing the development environment for sentiment analysis tasks.

Data Collection and Preprocessing

   - Acquiring movie review datasets.

   - Cleaning and preprocessing textual data for analysis.

Text Tokenization and Feature Extraction

   - Tokenizing text into words or subword units.

   - Creating feature representations of text for machine learning.

Building Sentiment Analysis Models

   - Introduction to machine learning algorithms for sentiment classification.

   - Implementing and training sentiment analysis models.

Model Evaluation

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

   - Cross-validation techniques for robust evaluation.

Handling Imbalanced Data

   - Strategies for dealing with imbalanced sentiment datasets.

   - Resampling and weighting techniques.

Real-World Applications

   - Applying sentiment analysis models to movie reviews from diverse sources.

   - Analyzing and visualizing sentiment trends in moviegoer opinions.

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