Extracting R’s learned LDA model parameters into word cloud visualisations.

R’s `topicmodels`

package contains an `LDA`

function for performing Latent Dirichlet Allocation on a text corpus. LDA separates a text corpus into *k* topics, assigning each document in the corpus a topic weight. Each topic in turn is represented by a set of term weights. I wanted to display the terms with the highest weight in a word cloud for each topic.

It wasn’t immediately apparent to me how to transform the results of LDA into a format suitable for creating a word cloud, so I’ve set out the missing piece of the puzzle below.

First, let’s run LDA on the example ‘Associated Press’ dataset and create 2 example topics:

```
library(topicmodels)
library(wordcloud)
data("AssociatedPress")
lda <- LDA(AssociatedPress, k = 2, control = list(seed = 1234))
```

The `k`

parameter specifies the number of topics we seek to separate the copus into. (For help on choosing an optimal `k`

, take a look at the ldatuning package).

Having created a trained `lda`

object, it’s trivially easy to extract the top terms for each topic using the `terms`

function:

```
terms(lda, 5)
# Topic 1 Topic 2
# [1,] "percent" "i"
# [2,] "million" "president"
# [3,] "new" "government"
# [4,] "year" "people"
# [5,] "billion" "soviet"
```

Whilst this is useful for inspecting returned topics, it lacks a key piece of information necessary for creating word clouds: the strength of each word in the topic. It’s not enough to know the rank, we also want to know the weight.

Much of the information about inspecting LDA results understandably focuses on the relationship between topics and documents (LDA after all being primarily a document classification algorithm). The topic-document weights are available in the `gamma`

variable:

```
head(lda@gamma)
# [,1] [,2]
# [1,] 0.2480616686 0.7519383
# [2,] 0.3615485445 0.6384515
# [3,] 0.5265844180 0.4734156
# [4,] 0.3566530023 0.6433470
# [5,] 0.1812766762 0.8187233
# [6,] 0.0005883388 0.9994117
```

Notice that each row of the `gamma`

matrix sum to 1.0. This contains, the proportion of each document’s weight that should be allocated to each topic.

It wasn’t immediately apparent to me that the `beta`

variable contains the same information for terms. The dimension of the `beta`

matrix is 2 x 10473, or the number of topics x the number of terms. However each column certainly doesn’t sum to 1.0:

```
lda@beta[,1]
# [1] -27.10812 -10.15299
```

Nor do the rows:

```
sum(lda@beta[1,])
# [1] -167587.3
```

It eventually dawned on me that the values in the beta matrix are actually the log transformation of the actual probabilities. If we transform the values in the other direction, with `exp`

, we discover that in fact the rows do sum to 1.0:

```
> sum(exp(lda@beta[1,]))
# [1] 1
```

Thus, we can infer that the beta matrix actually contains the proportion of each topic’s weight that should be allocated to each term. This is exactly the weight we want to plot on our word cloud. We can create a dataframe from the terms and values from the beta matrix corresponding to the topic we’re interested in, and sort by the probability descending:

```
topic <- 1
df <- data.frame(term = lda@terms, p = exp(lda@beta[topic,]))
head(df[order(-df$p),])
# term p
# 6838 percent 0.009806671
# 5957 million 0.006837635
# 6286 new 0.005942985
# 10423 year 0.005750201
# 982 billion 0.004267884
# 5292 last 0.003679708
```

This dataframe is ready for use in R’s `wordcloud`

library like so:

```
wordcloud(words = df$terms,
freq = df$p,
max.words = 200,
random.order = FALSE,
rot.per = 0.35,
colors=brewer.pal(8, "Dark2"))
```