Text Mining and Analytics: Pattern Matching & Information Extraction

Natural Language Processing    |    Intermediate
  • 12 Videos | 1h 52m 15s
  • Includes Assessment
  • Earns a Badge
Sometimes, business wants to find similar-sounding words, specific word occurrences, and sentiment from the raw text. Having learned to extract foundational linguistic features from the text, the next objective is to learn the heuristic approach to extract non-foundational features which are subjective. In this course, learn how to extract synonyms and hypernyms with WordNet, a widely used tool from the Natural Language Toolkit (NLTK). Next, explore the regex module in Python to perform NLTK chunking and to extract specific required patterns. Finally, you will solve a real-world use case by finding sentiments of movies using WordNet. After comleting this course, you will be able to use a heuristic approach of natural language processing (NLP) and to illustrate the use of WordNet, NLTK chunking, regex, and SentiWordNet.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    outline the heuristic approach for natural language processing (NLP)
    recall why WordNet is important
    illustrate and extract synonyms and identify WordNet hierarchies - hypernyms and hyponyms
    identify meronyms and holonyms
    demonstrate the lexical resource for opinion mining and finding the sentiment of text
  • demonstrate the Python RE Module, RE - Search, Find ALL, Finditer, Groups, Find and Replace, and Split
    demonstrate anchors, character classes, greedy, lazy and backtracking algorithms, and performance
    perform basic information extraction using NLTK chunking and regex rules
    perform advanced information extraction using NLTK chunking and regex rules
    model and find sentiment of movie plots using SentiWordNet
    summarize the key concepts covered in this course

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    1m 34s
    UP NEXT
  • Playable
    2. 
    A Heuristic Approach to NLP
    2m 27s
  • Locked
    3. 
    WordNet Fundamentals
    7m 21s
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    4. 
    Performing Synonyms, Synset, and WordNet Hierarchy
    10m 28s
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    5. 
    Performing WordNet Relations and Semantic Similarity
    13m 4s
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    6. 
    Working with SentiWordNet and Sentiment Analysis
    22m 6s
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    7. 
    Working with Regex for Pattern Matching
    10m 35s
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    8. 
    Investigating Python Regex Language
    16m 26s
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    9. 
    Performing Basic NLTK Chunking and Regex
    8m 39s
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    10. 
    Performing Advanced NLTK Chunking and Regex
    3m 44s
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    11. 
    Modeling Movie Plot Sentiment Analysis with WordNet
    14m 47s
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    12. 
    Course Summary
    1m 3s

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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