# Get the Top Words and Documents in Each Topic

### Description

This function takes a model fitted using
`lda.collapsed.gibbs.sampler`

and returns a matrix of the
top words in each topic.

### Usage

1 2 | ```
top.topic.words(topics, num.words = 20, by.score = FALSE)
top.topic.documents(document_sums, num.documents = 20, alpha = 0.1)
``` |

### Arguments

`topics` |
For |

`num.words` |
For |

`document_sums` |
For |

`num.documents` |
For |

`by.score` |
If |

`alpha` |

### Value

For `top.topic.words`

, a *num.words \times K* character matrix where each column contains
the top words for that topic.

For `top.topic.documents`

, a *num.documents \times K* integer matrix where each column contains
the top documents for that topic. The entries in the matrix are
column-indexed references into `document_sums`

.

### Author(s)

Jonathan Chang (slycoder@gmail.com)

### References

Blei, David M. and Ng, Andrew and Jordan, Michael. Latent Dirichlet allocation. Journal of Machine Learning Research, 2003.

### See Also

`lda.collapsed.gibbs.sampler`

for the format of `topics`.

`predictive.distribution`

demonstrates another use for a fitted
topic matrix.

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | ```
## From demo(lda).
data(cora.documents)
data(cora.vocab)
K <- 10 ## Num clusters
result <- lda.collapsed.gibbs.sampler(cora.documents,
K, ## Num clusters
cora.vocab,
25, ## Num iterations
0.1,
0.1)
## Get the top words in the cluster
top.words <- top.topic.words(result$topics, 5, by.score=TRUE)
## top.words:
## [,1] [,2] [,3] [,4] [,5]
## [1,] "decision" "network" "planning" "learning" "design"
## [2,] "learning" "time" "visual" "networks" "logic"
## [3,] "tree" "networks" "model" "neural" "search"
## [4,] "trees" "algorithm" "memory" "system" "learning"
## [5,] "classification" "data" "system" "reinforcement" "systems"
## [,6] [,7] [,8] [,9] [,10]
## [1,] "learning" "models" "belief" "genetic" "research"
## [2,] "search" "networks" "model" "search" "reasoning"
## [3,] "crossover" "bayesian" "theory" "optimization" "grant"
## [4,] "algorithm" "data" "distribution" "evolutionary" "science"
## [5,] "complexity" "hidden" "markov" "function" "supported"
``` |