Introduction

Text and sentiment analysis plays a pivotal role in understanding customer sentiment, market trends, and operational performance through unstructured data sources such as social media posts, customer feedback, reviews, and internal documents. By analyzing text and deriving sentiment, businesses can make more informed decisions in areas like marketing, customer service, and product development. This course will teach professionals how to use text mining, natural language processing (NLP), and sentiment analysis techniques to convert text data into actionable insights within business intelligence frameworks.

Objectives

By the end of this course, participants will be able to:

• Understand the role of text and sentiment analysis in BI.
• Use NLP techniques to process and analyze unstructured text data.
• Extract meaningful insights from text data sources (e.g., reviews, social media, emails).
• Perform sentiment analysis to gauge customer opinions and business trends.
• Integrate text and sentiment analysis outputs into BI tools (e.g., Power BI, Tableau).
• Utilize machine learning algorithms for improving sentiment analysis accuracy.
• Build automated text and sentiment analysis pipelines for scalable BI applications.

Who Should Attend?

This course is ideal for:

• Data scientists and analysts in BI roles
• Business intelligence professionals and BI developers
• Marketing and customer experience managers
• IT professionals supporting BI systems
• Anyone looking to enhance their ability to leverage unstructured text data for better decision-making

Course Outline

Day 1: Introduction to Text and Sentiment Analysis in BI

• What is Text and Sentiment Analysis?
  • Text Mining vs. Text Analytics
  • Overview of Sentiment Analysis and its Business Value
• Types of Unstructured Text Data:
  • Customer Feedback, Reviews, Social Media, Support Tickets, Emails
• The Role of Text and Sentiment Analysis in Business Intelligence
• Key Concepts in Text Analytics: Tokenization, Lemmatization, Named Entity Recognition (NER)
• Overview of Sentiment Analysis: Polarity, Subjectivity, Emotion Detection
• Case Study: How Leading Brands Leverage Sentiment Analysis for Marketing Insights
• Hands-on Session: Exploring Text Data with Python (NLTK, SpaCy)

Day 2: Natural Language Processing (NLP) Techniques for Text Analysis

• Introduction to NLP: What is NLP and Why It’s Important for Text Analysis
• Text Preprocessing: Cleaning and Preparing Text Data (Stop Word Removal, Lemmatization, etc.)
• Tokenization: Breaking Text into Meaningful Units (Words, Sentences)
• Text Vectorization: TF-IDF and Word Embeddings (Word2Vec, GloVe)
• Named Entity Recognition (NER): Extracting Key Information from Text (People, Locations, Organizations)
• Text Classification: Categorizing Text Data for Insights (Spam Detection, Topic Categorization)
• Case Study: Improving Customer Support with Automated Text Classification
• Hands-on Workshop: Preprocessing and Vectorizing Text Data Using Python

Day 3: Sentiment Analysis Techniques

• What is Sentiment Analysis?: Understanding Sentiment Classification (Positive, Negative, Neutral)
• Emotion Detection: Going Beyond Polarity – Detecting Emotions such as Joy, Sadness, and Anger
• Sentiment Lexicons: Using Pre-built Lexicons for Sentiment Scoring (VADER, SentiWordNet)
• Supervised vs. Unsupervised Sentiment Analysis: Using Machine Learning vs. Rule-Based Methods
• Building Sentiment Models: Using ML Algorithms (Naive Bayes, SVM, Random Forest)
• Deep Learning in Sentiment Analysis: Leveraging RNNs and LSTMs for Improved Accuracy
• Case Study: Sentiment Analysis in Social Media for Brand Health Monitoring
• Hands-on Session: Building a Sentiment Analysis Model Using Python (Scikit-learn and Keras)

Day 4: Integrating Text and Sentiment Analysis with BI Tools

• Connecting Text Data to BI: Methods for Importing and Analyzing Text Data in BI Tools
• Sentiment Analysis Dashboards: Visualizing Sentiment Data in BI Dashboards (Power BI, Tableau, Qlik)
• Automated Reporting: Creating Reports and Alerts Based on Sentiment Trends
• Integrating Sentiment Scores into Business Performance Dashboards
• Real-Time Sentiment Analysis: Leveraging Streaming Data for Live Sentiment Tracking
• Best Practices for Data Visualization: Displaying Sentiment Trends and Key Insights
• Case Study: Integrating Social Media Sentiment into Business Intelligence Dashboards
• Hands-on Session: Integrating Sentiment Analysis into Tableau for Visualization

Day 5: Advanced Topics and Future Trends in Text and Sentiment Analysis

• Deep Learning for Text and Sentiment Analysis: Transformer Models like BERT, GPT for NLP
• Multilingual Sentiment Analysis: Handling Text Data in Multiple Languages
• Text Summarization: Extractive and Abstractive Summarization for Business Insights
• Topic Modeling: Discovering Hidden Topics in Large Text Datasets (LDA, NMF)
• Voice Sentiment Analysis: Analyzing Audio and Voice Data for Customer Feedback
• Future Trends in Text and Sentiment Analysis: AI and NLP in BI, Real-Time Analytics, and Chatbots
• Ethical Considerations in Sentiment Analysis: Bias, Accuracy, and Privacy Concerns
• Final Project: Building a Text and Sentiment Analysis BI Solution for Your Organization