Executive Development Programme in Building Sentiment Analysis Tools with Python

This course is designed for executives and mid-level managers looking to leverage Python for building sentiment analysis tools. Participants will gain hands-on experience in applying machine learning models to text data, enabling them to analyze customer feedback, social media trends, and more, to drive strategic business decisions.

By the end of the program, attendees will be proficient in using Python libraries such as NLTK, scikit-learn, and spaCy, and will have developed a sentiment analysis project that can be directly applied to their organization's needs.

1. 1. Introduction to Sentiment Analysis: Learners will understand the basics of sentiment analysis, including its importance in text analytics. They will gain foundational knowledge of text data preprocessing techniques using Python.
2. 2. Python Programming for Data Science: This module covers essential Python libraries for data science, including Pandas, NumPy, and Matplotlib. Learners will gain practical skills in handling and visualizing text data.
3. 3. Text Data Preprocessing: Learners will learn how to clean and preprocess text data, including tokenization, stemming, and lemmatization. Practical skills include implementing these techniques using NLTK and SpaCy.
4. 4. Natural Language Processing (NLP) Fundamentals: This module introduces key NLP concepts such as named entity recognition, part-of-speech tagging, and sentiment lexicons. Learners will apply these concepts to real-world text datasets.
5. 5. Building Sentiment Analysis Models: Learners will create basic sentiment analysis models using machine learning techniques like Naive Bayes and Support Vector Machines (SVM). Practical skills include model training, evaluation, and deployment.
6. 6. Advanced Text Feature Extraction: This module covers advanced text feature extraction methods, including TF-IDF, word embeddings (Word2Vec and GloVe), and Doc2Vec. Learners will implement these techniques to improve model performance.
7. 7. Deep Learning for Sentiment Analysis: Learners will explore deep learning models for sentiment analysis, including Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks. Practical skills include building and training deep learning models using TensorFlow or PyTorch.
8. 8. Model Evaluation and Optimization: This module focuses on evaluating and optimizing sentiment analysis models. Learners will learn about cross-validation, hyperparameter tuning, and other techniques to ensure robust model performance.
9. 9. Deploying Sentiment Analysis Tools: Learners will learn how to deploy sentiment analysis tools in real-world applications. Practical skills include using cloud services (like AWS or Google Cloud) and integrating models into web applications.
10. 10. Case Studies and Project Work: In this final module, learners will work on a comprehensive project, applying all the skills learned throughout the program. They will analyze real datasets, implement sentiment analysis tools, and present their findings.