Executive Development Programme in Text Mining and Sentiment Analysis with Python

This course is designed for business executives, data analysts, and managers seeking to leverage text mining and sentiment analysis for strategic decision-making. Participants will gain hands-on experience with Python, including data preprocessing, text vectorization, and model building for predictive analytics.

Attendees will learn to extract insights from unstructured text data, automate sentiment analysis for customer feedback, and enhance market research. By the end, they will be equipped to implement these techniques to drive business growth and improve customer satisfaction.

1. 1. Introduction to Text Mining and Sentiment Analysis: Learners will be introduced to the basics of text mining and sentiment analysis, understanding key terminologies and foundational concepts. They will gain skills in recognizing and defining text data problems and the importance of data preprocessing.
2. 2. Data Preprocessing for Text Mining: This module covers essential data cleaning and preprocessing techniques such as text normalization, tokenization, and stop word removal. Learners will develop practical skills in preparing text data for analysis.
3. 3. Exploratory Data Analysis (EDA) for Text Data: Learners will conduct exploratory data analysis on text datasets using Python libraries. They will learn to visualize text data and extract insights to inform further analysis.
4. 4. Text Vectorization Techniques: This module introduces various text vectorization methods including Bag of Words, TF-IDF, and word embeddings. Learners will gain hands-on experience in converting text data into numerical vectors suitable for machine learning models.
5. 5. Sentiment Analysis with Rule-Based Approaches: Learners will explore and implement rule-based methods for sentiment analysis, including the use of lexicons and dictionaries. Practical skills in creating and evaluating sentiment analysis models using these techniques will be developed.
6. 6. Machine Learning Models for Sentiment Analysis: This module covers the application of machine learning models for sentiment analysis, including Naive Bayes, SVM, and logistic regression. Learners will learn to build and evaluate these models on real-world datasets.
7. 7. Deep Learning for Text Mining: Learners will delve into deep learning models for text mining, focusing on neural networks and recurrent neural networks (RNNs). Practical skills in training and optimizing deep learning models for text analysis will be developed.
8. 8. Advanced Text Mining Techniques: This module covers advanced topics such as topic modeling (LDA), named entity recognition, and document clustering. Learners will gain skills in applying these techniques to extract meaningful insights from large text corpora.
9. 9. Text Mining Case Studies and Applications: Through case studies, learners will apply the skills learned in previous modules to real-world scenarios, understanding the practical implications and challenges of text mining projects.
10. 10. Project Development and Presentation: Learners will work on a comprehensive project that integrates the knowledge and skills acquired throughout the programme. They will develop a final project, present their findings, and receive feedback from peers and instructors.