Text Analytics and Natural Language Processing (NLP) with R

Name: Text Analytics and Natural Language Processing (NLP) with R
Rating: 4.1 (34 reviews)

Introduction to Text Analytics, Web Scraping, Corpus Analytics, Natural Language Processing (NLP) Techniques

Created byBeeharry Shiam

Last updated 1/2022

English

English [Auto],

What you'll learn

Students will be able to extract big text data from different sources for analysis
Text Pre-processing and understanding different types of text data
Using most common R packages for text analytics
Students will be able to create customized word cloud
Perform corpus analysis
Perform text summarization techniques
Perform part of speech tagging
Carry out Emotion and sentiment analysis
Understand and use language models
Perform web scraping for text data (customer reviews from web sites)
Natural language processing for linguists
Natural Language processing for computational linguists
Analyse multilingual text data
Future trends in NLP and CL

Course content

7 sections • 49 lectures • 8h 38m total length

Introduction to the course8:19
This is a welcoming message to the course. Students are given an insights of the course and how it will prepare them for landing into the appropriate jobs and embark on the right project with the right skills.
Applications of Text Analytics and NLP6:42
This lecture will give students an insights of the subject matter that is Text Analytics and Natural Language Processing. At the end , students will have to think about a project or a specific applications of Text Analytics and/or NLP they will wish to embark into. They will have an idea of where this course will lead them to in the field of Text analytics and NLP.
RStudio Environment - Part 115:01
RStudio Environment - Part 210:05
Data Types in R - Part 113:09
Data Types in R - Part 29:42
Discover how R objects carry attributes such as names, dimensions, and class, and compare data frames with the table data type. Learn to install, convert, view datasets, and write functions.
Importing Text Data in RStudio - Part 114:51
Importing Text Data in RStudio - Part 215:01

Basic Text Manipulation10:46
Master basic text manipulation in R using paste for concatenation with separators or collapse, extract substrings, replace segments, and split strings, with packages like stringr and stringi.
Regular Expression with R - Part 112:53
Explore how to use regular expressions in R to detect, extract, and replace matching strings, using grep, escape sequences, and basic regex concepts.
Regular Expression with R - Part 214:07
Using Pipes in R - Part 111:29
Using Pipes in R - Part 214:53
use the pipe operator in r to chain commands for selecting columns and filtering by continent, then mutate and summarize gdp per capita and life expectancy.
Read Text files with ReadText package13:30

String Manipulation with R - Part 110:33
Master basic text pre-processing in R, including string manipulation, abbreviation, word and sentence tokenization, word length, stemming with snowball, and frequency analysis to prepare text data for analytics.
String Manipulation with R - Part 27:26
Tokenization and N-grams concept11:15
Explore tokenization in text analytics using R, creating tokens from words, phrases, or sentences, and generate n-grams such as bi-grams and skip-grams while handling punctuation and stop words from tweets.
Stemming Concept12:17
Discover stemming, the process of reducing inflected words to their root forms to improve text analytics accuracy. The lecture demonstrates applying R-based stemming with different algorithms and dictionaries.
Lemmatization Concept10:04
Using Stopwords in R7:57

Web Scraping and CSS Selectors - Part 112:49
Web Scraping and CSS Selectors - Part 28:46
Job Site - Indeed9:11
Learn to scrape Indeed to extract job titles, company names, locations, and descriptions for the City of Toronto, loop through pages, build a data frame, and save and clean results.
Song Lyrics6:46
Learn to scrape song lyrics from a website with r: load libraries, parse pages, extract titles and links, fetch lyrics, and compile a data frame with title, link, and lyrics.
Tripadvisor Customer Reviews11:43

Introduction to Corpus Analytics9:02
Discover corpus analytics in r by working with a corpus, performing tokenization, frequency analysis, and n-grams, and exploring sentiment analysis or word clouds with quanteda, text2vec, tidytext, and tm.
Create a word cloud - Part18:45
Create a word cloud - Part28:24
Corpus Analytics Part 114:12
Analyze a Gutenberg Project novel with R using the corpus package, load and tokenize text, extract chapters, clean punctuation, and compute chapter-level token, type, and sentence statistics.
Corpus Analytics Part 211:47
Part 1 - Corpus Analytics of multiple documents13:29
Part 2 - Corpus Analytics of multiple documents12:19
Explore corpus analytics of multiple documents in R, computing IDF metrics, extracting key terms, performing sentiment analysis with tidytext lexicons, and visualizing by-grams and word networks.
Quanteda Package - Part 114:16
Learn to use the quanteda package to work with corpora. Explore creating, summarizing, subsetting, and querying corpora with kwic and regular expressions.
Quanteda Package - Part 214:18
Master quanteda part 2: search keywords with wildcards and multiword phrases; build document feature matrices with DSM, apply stop word removal and stemming, and explore word clouds and clustering.
Quanteda Package - Part 35:04

Reading about the concept13:48
Sentiment Analysis - Part 112:17
Sentiment Analysis - Part 26:33
Fake News Analysis10:05
Document Similarity - Search Engine Example6:18
Named Entity Recognition (NER)6:57
Part Of Speech Tagging - (POS)11:00
SpacyR Package for NLP - Part 113:05
Explore the spacyr wrapper for spaCy in R, enabling non-destructive tokenization, part-of-speech tagging, named-entity recognition, and noun-phrase extraction with multilingual models.
SpacyR Package for NLP - Part 26:12
UDPipe Package - Part 111:23
UDPipe Package - Part 29:24

Requirements

Should have basic experience of R and RStudio
Basic knowledge of HTML
Should have interest in text analytics and natural language processing
Should be aware of deriving insights from big text data
Should have a basic knowledge of programming skills
Should be interested in learning new languages for analytics
Should be interested in computational linguistics

Description

This course is for all who wants to explore and pursue a career in Text Analytics and Natural Language Processing with R. The course will drive you through all the techniques you need to process structured and unstructured text data and corpora. It will also teach you how to deal with multi-lingual text.

Who this course is for:

This course is for the general public who are keen to discover the world of big text data
This course is also designed for translators, journalists, linguists and multi-linguists
This course is also for historians, humanities and digital humanities students
All those who are interested in designing and generating natural language processing

Text Analytics and Natural Language Processing (NLP) with R

What you'll learn

Explore related topics

Course content

Introduction to Text Analytics and Natural Language Processing8 lectures • 1hr 33min

Basic Text Manipulation with R6 lectures • 1hr 18min

Text Preprocessing Techniques6 lectures • 1hr

Web Scraping Techniques5 lectures • 49min

Corpus Analytics10 lectures • 1hr 52min

Natural Language Processing11 lectures • 1hr 47min

Trends in Text Analytics and Natural Language Processing3 lectures • 20min

Requirements

Description

Who this course is for: