Text Analytics for Business Intelligence

Module 1: Introduction to Text Analytics (4 hours)

 Content:

o Overview of text analytics in business intelligence.

o Types of text data and their business value.

o Text preprocessing techniques (tokenization, stemming, lemmatization).

 Module Objectives (MOs):

1. Understand the importance of text analytics in BI.

2. Identify sources of text data for business use cases.

3. Preprocess text data for analysis.

5. Set up Python environments for text analytics workflows.

Module 2: Sentiment Analysis for Business Insights (6 hours)

 Content:

o Fundamentals of sentiment analysis.

o Lexicon-based and machine learning-based approaches.

o Case studies in customer sentiment extraction.

Module Objectives (MOs):

1. Build sentiment analysis models for business applications.

2. Compare lexicon-based and ML-based techniques.

3. Extract actionable insights from customer feedback.

4. Visualize sentiment trends for decision-making.

5. Evaluate the effectiveness of sentiment analysis tools.

Module 3: Topic Modeling and Trend Detection (6 hours)

 Content:

o Latent Dirichlet Allocation (LDA) and Non-Negative Matrix Factorization (NMF).

o Trend detection from social media and news articles.

o Applications in marketing and customer support.

 Module Objectives (MOs):

1. Implement topic modeling algorithms for text data.

2. Detect emerging trends in business-relevant datasets.

3. Use topic models for market research and customer insights.

4. Optimize models for large-scale text corpora.

5. Validate trend detection models with real-world data.

Module 4: Advanced Text Analytics Techniques (6 hours)

Content:

o Named Entity Recognition (NER) and entity linking.

o Text classification with machine learning.

 Module Objectives (MOs):

1. Develop NER models for extracting key entities.

2. Classify text data for targeted business strategies.

3. Customize pipelines for specific industry use cases.

4. Evaluate the performance of advanced text analytics models.

5. Integrate NER and classification results into BI dashboards.

Module 5: Visualization and Reporting of Text Analytics Results (6 hours)

 Content:

o Visualizing text data insights with tools like Tableau and Power BI.

o Automating text analytics workflows and reports.

o Best practices for presenting findings to stakeholders.

 Module Objectives (MOs):

1. Create visualizations to communicate text insights effectively.

2. Automate reporting workflows for consistency.

3. Design dashboards integrating text analytics.

4. Ensure clarity and impact in stakeholder presentations.

5. Evaluate the usability of text analytics reports.

Module 6: Practical Workshop: Building a Text Analytics Solution (5 hours)

Content:

o Developing a text analytics pipeline from preprocessing to visualization.

o Solving a business case using real-world datasets.

o Presenting outcomes and actionable recommendations.

 Module Objectives (MOs):

1. Build a complete text analytics solution for a business problem.

2. Address challenges in analyzing unstructured data.

3. Optimize pipelines for scalability and efficiency.

4. Document and present workflows effectively.

5. Reflect on project outcomes and propose improvements.