Text Mining with R: A Tidy Approach by Julia Silge & David Robinson

Author:Julia Silge & David Robinson [Silge, Julia]
Language: eng
Format: azw3
Publisher: O'Reilly Media
Published: 2017-06-12T04:00:00+00:00

library(tidytext) ap_topics <- tidy(ap_lda, matrix = "beta") ap_topics

## # A tibble: 20,946 × 3 ## topic term beta ## <int> <chr> <dbl> ## 1 1 aaron 1.686917e-12 ## 2 2 aaron 3.895941e-05 ## 3 1 abandon 2.654910e-05 ## 4 2 abandon 3.990786e-05 ## 5 1 abandoned 1.390663e-04 ## 6 2 abandoned 5.876946e-05 ## 7 1 abandoning 2.454843e-33 ## 8 2 abandoning 2.337565e-05 ## 9 1 abbott 2.130484e-06 ## 10 2 abbott 2.968045e-05 ## # ... with 20,936 more rows

Notice that this has turned the model into a one-topic-per-term-per-row format. For each combination, the model computes the probability of that term being generated from that topic. For example, the term “aaron” has a 1.686917 × 10-12 probability of being generated from topic 1, but a 3.8959408 × 10-5 probability of being generated from topic 2.

We could use dplyr’s top_n() to find the 10 terms that are most common within each topic. As a tidy data frame, this lends itself well to a ggplot2 visualization (Figure 6-2).

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