The following coursebook is the main part for Online Data Science Series: Topic Modeling for Text Analysis in R workshop produced by the team at Algoritma . Algoritma is a data science education center based in Jakarta. We organize workshops and training programs to help working professionals and students gain mastery in various data science sub-fields: data visualization, machine learning, data modeling, statistical inference, etc.
This 3-days online workshop is a beginner-friendly introduction to Topic Modeling using R. By performing topic model you can organize, understand and summarize large collections of textual information from your text data.
- Introduction to R Programming Language
- Working with R Studio Environment
- Using R Markdown for reproducible research
- Inspecting data structure
- Essence of Text Mining or Natural Language Processing
- Working with a text corpus, a large and structured set of texts
- Preparing your text data: data cleansing and manipulation
- Word-tokenizing to identify word’s meaning
- Using visualization to analyse text data
- Examples of utilizing topic modeling in various industries
- Understanding the principles and workflow of topic modeling
- Understanding LDA (Latent Dirichlet Allocation), the algorithm behind topic modeling
- Exploring & Interpreting the output of a topic model
Following packages are used within the material:
if (!require("pacman")) install.packages("pacman")
pacman::p_load_gh("trinker/lexicon","trinker/textclean")
packages <- c("rmarkdown", "ggplot2", "dplyr", "lubridate", "stringr", "tidyr", "tidytext", "SnowballC", "hunspell", "textmineR", "ggwordcloud", "scales")
install.packages(packages)