Text Mining and Analytics: Natural Language Processing Libraries

Natural Language Processing    |    Intermediate
  • 13 videos | 1h 59m 1s
  • Includes Assessment
  • Earns a Badge
Rating 4.4 of 5 users Rating 4.4 of 5 users (5)
There are many tools available in the Natural Language Processing (NLP) tool landscape. With single tools, you can do a lot of things faster. However, using multiple state-of-art tools together, you can solve many problems and extract multiple patterns from your data. In this course, you will discover many important tools available for NLP such as polyglot, Genism, TextBlob, and CoreNLP. Explore their benefits and how they stand against each other for performing any NLP task. Learn to implement core linguistic features like POS tags, NER, and morphological analysis using the tools discussed earlier in the course. Discover defining features of each tool such as multiple language support, language detection, topic models, sentiment extractions, part of speech (POS) driven patterns, and transliterations. Upon completion of this course, you will feel confident with the Python tool ecosystem for NLP and will be able to perform state-of-art pattern extraction on any kind of text data.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Recognize polyglot and textblob and outline the benefits of these options over natural language toolkit (nltk) and spacy with use cases
    Explain the existence of gensim and corenlp and describe the benefits of these options over nltk and spacy with use cases
    Install linguistic features including named entity recognition (ner), part of speech (pos) tagging, morphological analysis, and multiple languages support
    Demonstrate multi-language part of speech tagging and morphological analysis using polyglot
    Demonstrate multi-language parts of speech tagging using polyglot including language detection, sentiment analysis, and transliteration
    Install linguistic features including noun phrase extraction, pos, parsing, and wordnet integration
  • Demonstrate additional features of textblob including sentiment analysis, classification models, tokenization, word/phrase frequencies, word inflection, and spelling correction
    Demonstrate installation and topic modeling with gensim
    Demonstrate building bigram and trigram for topic modeling using genism
    Demonstrate building an lda model for topic modeling using genism
    Demonstrate advanced genism features such as identifying query similarity
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 1m 39s
  • 6m 8s
  • Locked
    3.  Introduction to Gensim and CoreNLP
    4m 9s
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    4.  Using Basic Polyglot Features
    13m 18s
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    5.  Using Multi-language Part of Speech Tagging
    9m 34s
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    6.  Exploring Advanced PolyGlot Features
    14m 24s
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    7.  Implementing Basic TextBlob Features
    9m 12s
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    8.  Implementing Advanced TextBlob Features
    9m 45s
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    9.  Exploring Basic Gensim Features
    12m 23s
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    10.  Building bigram and trigram Using Gensim
    11m 56s
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    11.  Building an LDA Model for Topic Modeling
    12m 15s
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    12.  Exploring Advanced Gensim Features
    13m 33s
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    13.  Course Summary
    47s

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