Description Usage Arguments Details Value References

View source: R/topic_summaries.R

The classical way of representing topics by order by the probability for a type within a topic.

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`state` |
A tidy topic model state file |

`j` |
The number of top words to return |

`beta` |
Beta hyper parameter. Default is 0 (no smoothing). |

`lambda` |
Relevance weight. Default is 0.6. |

To save space the calculations are done using a sparse format,
only returning values for type-topic combination that exist in the model.
This means that unless `beta`

is set to 0, the returning probabilities
will not sum to 1.

Not all reweighting schemes return a probability (such as KR1, KR2 and relevance)

Returns a data_frame with topic and top terms

Topic and keyword re-ranking for LDA-based topic modeling (2009) LDAvis: A method for visualizing and interpreting topics (2014)

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