256 Text Mining and Visualization: Case Studies Using Open-Source Tools
terms(model_lda, 10)
Topic 1 Topic 2 Topic 3 Topic 4 Topic 5
[1,] "wire" "god" "imag" "israel" "game"
[2,] "car" "key" "file" "drug" "gun"
[3,] "jim" "encrypt" "window" "launch" "team"
[4,] "sale" "moral" "drive" "station" "stephanopoulo"
[5,] "circuit" "church" "card" "mission" "armenian"
[6,] "cabl" "jesus" "color" "orbit" "player"
[7,] "pin" "homosexu" "format" "arab" "fan"
[8,] "insur" "jew" "graphic" "isra" "steve"
[9,] "electr" "clipper" "jpeg" "shuttl" "tax"
[10,] "bike" "fbi" "display" "food" "turkish"
#Display the top 5 topics in each document
topics(model_lda, 5)
Looking at the first 10 terms in each of the 5 topics created, you can inspect whether
the topics make sense. A