Key Points

Introduction

  • Know what text mining is
  • Know what stopwords are
  • Knowledge of the data we are working with
  • Know what tidy text means

Loading data

  • Packages must be installed and loaded
  • The dataset needs to be loaded
  • The dataset can be inspected by means of different functions

Tokenisation and stopwords

  • Know how to prepare text for analysis

Word frequency analysisProportionVisualization

  • Making a frequency analysis
  • Visualising the results

Sentiment analysisn-grams and correlations

  • Sentiments is the emotion or tone in a text
  • There are different lexicons
  • It is possible to add sentiments to words
  • It is possible to visualise the sentiments
  • It is possible to look at relationsship between words

Whats next?

  • Use .md files for episodes when you want static content
  • Use .Rmd files for episodes when you need to generate output
  • Run sandpaper::check_lesson() to identify any issues with your lesson
  • Run sandpaper::build_lesson() to preview your lesson locally