Text and Web Analytics

Text and Web Analytics

1. Text Mining Application Area

   • Information Extraction
   • Topic Tracking
   • Summarization
   • Categorization
   • Clustering
   • Concept Linking
   • Question Answering

2. Natural Language Processing

   • Categories
     • Information Retrieval/Extraction
     • Named-entity recognition
     • Question answering
     • Automatic subset of information extraction
     • Natural language generation & understanding

3. Text mining

   • Process
     • Step 1:Establish the corpus
     • Step 2: Create the term document matrix
     • Step 3: Extract Knowledge
   • Challenges
     • Analysis of free-form text
     • Ambiguities
     • Semantic analysis
     • Syntax analysis

4. Web Mining

   • Challenges
     • Web is too big
     • Web is too complex
     • Web is too dynamic
     • Web is not specific to a domain
     • Web has everything
   • Categories
     • Web content mining
     • Web structure mining
     • Web usage mining
   • CRISP-DM
     • Step 1:Business Understanding
     • Step 2: Data understanding
     • Step 3: Data preparation
       • Issues
         • Differentiate individual user session
         • Identify unwanted log file entries
         • Data transformation
     • Step 4: Modelling
     • Step 5: Evaluation
     • Step 6: Deployment