This specialized course focuses on extracting insights from unstructured text data using natural language processing and sentiment analysis techniques. Participants will learn to process text data, identify key themes, and measure sentiment across customer feedback, social media, and other textual sources. The curriculum covers text preprocessing, topic modeling, sentiment classification, and advanced NLP methods for business intelligence. Through practical applications, learners will develop the skills to transform qualitative text data into quantitative insights that inform business strategies.
Text and Sentiment Analytics
Data Analytics and Business Intelligence
October 25, 2025
Introduction
Objectives
Key learning objectives include:
- Preprocess and clean text data for analysis
- Implement sentiment analysis algorithms
- Extract key topics and themes from text corpora
- Build text classification models
- Analyze social media and customer feedback data
- Create visualizations for text analytics results
- Apply named entity recognition techniques
- Develop text analytics pipelines for business applications
Target Audience
- Data scientists and analysts
- Marketing and social media analysts
- Customer experience professionals
- Product managers and researchers
- Business intelligence developers
- Digital marketing specialists
- Academic researchers in social sciences
Methodology
The course combines theoretical NLP concepts with practical applications using real text datasets from customer reviews, social media, and business documents. Participants work through scenarios analyzing sentiment and extracting insights from various text sources. Case studies demonstrate text analytics applications across industries, while group activities focus on collaborative analysis projects. Individual exercises build technical skills, and mini-case studies present specific text analysis challenges. Syndicate discussions explore interpretation and business implications of text analytics results.
Personal Impact
- Enhanced ability to extract insights from text data
- Improved skills in natural language processing
- Stronger understanding of sentiment analysis techniques
- Increased proficiency with text analytics tools
- Better interpretation of qualitative feedback
- Developed ability to build text analysis pipelines
Organizational Impact
- Deeper understanding of customer opinions and needs
- Improved customer experience through feedback analysis
- Enhanced brand monitoring and reputation management
- Better market intelligence from text sources
- Reduced manual review of customer feedback
- Increased responsiveness to market trends
Course Outline
Unit 1: Text Analytics Fundamentals
NLP Basics- Text data sources and collection
- Text preprocessing techniques
- Tokenization and normalization
- Corpus creation and management
Unit 2: Text Representation
Feature Engineering- Bag-of-words representation
- TF-IDF weighting
- Word embeddings (Word2Vec, GloVe)
- Document-term matrix creation
Unit 3: Sentiment Analysis
Sentiment Techniques- Lexicon-based approaches
- Machine learning classification
- Aspect-based sentiment analysis
- Sentiment visualization
- Customer review analysis
- Social media monitoring
- Brand sentiment tracking
- Competitive analysis
Unit 4: Topic Modeling
Theme Extraction- Latent Dirichlet Allocation (LDA)
- Non-negative Matrix Factorization
- Topic interpretation and labeling
- Dynamic topic modeling
Unit 5: Advanced NLP Techniques
Entity and Relationship Extraction- Named Entity Recognition
- Relationship extraction
- Text summarization
- Question answering systems
Unit 6: Business Applications
Industry Use Cases- Customer service optimization
- Market research and intelligence
- Product feedback analysis
- Reputation management
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