Text Analytics and Natural Language Processing (NLP) with R


Course Number: RPROG-114

Duration: 3 days (19.5 hours)

Format: Live, hands-on

NLP Training Overview

Accelebrate's Natural Language Processing (NLP) with R training course teaches attendees how to use R programming to explore and analyze text data.  This class comprehensively covers methods for ingesting text data from a variety of sources such as plain text files, pdfs, or the web, and then processing that data using the latest natural language processing and deep learning techniques.

Location and Pricing

Accelebrate offers instructor-led enterprise training for groups of 3 or more online or at your site. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for private corporate training on-site or online, please contact us.

In addition, some courses are available as live, online classes for individuals. See a schedule of online courses.

Objectives

Students will be able to

  • Import text data from a variety of source formats
  • Tokenize text data to meaningful units
  • Wrangle text data using specific textual functions
  • Compute aggregating measures on tokenized data
  • Translate between text data formats
  • Complete a sentiment analysis
  • Perform document classification
  • Perform topic modeling
  • Built a simple neural network appropriate for NLP modeling

Prerequisites

Students must have completed Accelebrate's Intro to R Programming training or have the equivalent experience. Students should have a working knowledge of the R language, RStudio, and the dplyr/tidyverse packages.

Outline

Expand All | Collapse All

Working with unstructured text data
  • string methods
  • regex
  • reading in text files
  • review of base (R/Python)
Importing
  • parsing data from a text file
  • importing it into a tidy structure
  • parsing data from a pdf
    • From a “pile of pdfs”
  • scraping data from the web
  • Discussion of other methods
    • OCR
    • Handwriting recognition
Managing Text Data 1
  • a tidy text format
  • Overview of text data formats
    • tidy text
    • token list
    • Bag of words
    • document term matrix or document frequency matrix (dfm/dt)
    • corpus
    • docvars
  • associated formats
    • stop words
    • Sentiment lexica
    • word vectors / models
Managing Text Data 2
  • tokenizing text
  • units of tokenization
    • tokens
    • lemma
    • stems
    • n-grams
    • sentences
    • Tweets
  • Tf-idf
  • Log-odds (tidylo)
Sentiment Analysis
  • Sentiment lexica
  • Sentiment analysis with inner_join
  • Analyzing by other units
  • Valence shifting
  • VADER
Document Classification
  • Text similarity - stringiest
    • Cosine
    • Edit distance
  • Machine Learning for document classification
    • Naive Bayes model
Topic Modeling / Document Clustering
  • LDA
  • stm
Text and Deep Learning
  • Deep learning introduction
  • Architecture of neural networks
  • Tensorflow + keras
  • Word vectors
    • word2vec
    • Text2vec
    • GloVe
    • Spacy
  • Combining Deep Learning and NLP
    • CNN
    • RNN
    • LSTM
  • Named Entity Recognition (NER)
  • Part of Speech tagging (POS)
  • Dependency Parsing
Conclusion

Training Materials:

All R Programming training students receive a copy of O’Reilly's Text Mining with R and related courseware.

Software Requirements:

  • R 3.5 or later with console
  • IDE or text editor of your choice (RStudio recommended)


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