Introduction
  • What is Natural Language Processing ( NLP )?
  • How To Make The Most Of This Course
  • Sneak Peek Of What We Will Do In This Course
Rapidminer
  • Why Use Rapidminer?
  • Difference Between Rapidminer and Python
  • Installing Rapidminer
  • Rapidminer Development Environment - Introduction
  • Rapidminer - Operators, Extensions, Repository, Parameters, Help
  • Install The Text Mining Extension
  • Download The Data
  • Let's Build A Basic Process
Text Classification - Detecting Spam From Text Messages
  • What Will You Learn From This Exercise?
  • Downloading The Data
  • Importing Data
  • Change Data Attribute Role And Type
  • Creating Word List And Word Vector
  • Balance Your Dataset
  • Building The First Spam Detector
  • Validate Our Spam Detector
  • Measure The Performance Of The Spam Detector
  • Optimize Performance of Your Spam Detector
  • Conclusion - Spam Detection
  • Additional Reading
Quiz
  • Let's test your knowledge
Analyze The Sentiment Of Stocks In Your Watchlist
  • What Will You Learn From This Exercise?
  • Connecting To The Twitter API
  • Install Extension For Text Mining and Create API Account
  • Downloading The Data
  • About The Data
  • Import The Historical Data
  • Search For Tweets About Company In Your Watchlist
  • Clean and Prepare Text in Tweets
  • Find Sentiment Of The Tweets
  • Gather Required Data To Calculate Sentiment Score
  • Generate a Sentiment Score For The Stock
  • Conclusion - Analyze The Sentiment Of Stocks In Your Watchlist
Bonus - Scraping Web pages For Text Data
  • Install Web Mining Extension
  • Extracting Text From a Webpage or RSS Feed
Quiz
  • Let's test your knowledge
Building a Scorecard For Customer Retention Agents
  • What Will You Learn From This Exercise?
  • Downloading The Data
  • What Is This Data?
  • Importing The Data
  • Prepare Text - Change Type
  • Cleaning Special Characters From Text
  • Count Number of Words in Each Conversation
  • Removing Scripted Conversations
  • Remove Disconnected Calls
  • Extract Entities from Call Logs
  • Find Sentiment Of Call Logs
  • Save Your Process
  • Exploring Our Text Data - Theory
  • Exploring Our Text Data - Hands On
  • Create Metrics - Agent List & Sentiment Score
  • Create Metrics - Average Word Count by Agent
  • Create Metrics - Average Time On Call by Agent
  • Create Metrics - Customers Retained Count by Agent
  • Create Metrics - Customer Greeted By Title Count by Agent
  • Create Metrics - Calculate Total Calls by Agent
  • Replace Missing Values
  • Creating The Scores
  • Score Carding Retention Agents
  • Presenting Our Results
  • Identifying Competitors From Identified Entities
  • Conclusion - Scorecard For Customer Retention Specialist
Bonus - Solution Files
  • Downloading The Solution Files
  • How To Use The Solution Files
Bonus - Research Papers and Additional Reading
  • Research Papers and Additional Reading
Thank You
  • Thank You!