Mean topic likelihoods summary — topics

topics_summary summarizes the mean likelihood of topics across all documents combined with a set of suitable labels. This is a convenience function to create summary visualizations or interactive tables.

topics_summary(topicsByDocDate, topicLabels)

Arguments

topicsByDocDate	a dataframe as returned by `topics_by_doc_date`
topicLabels	a dataframe as returned by `topics_terms_map`, associating a `topic_id` with a suitable `topic_label`.

Value

@return a dataframe with term frequencies by chosen timebin, where:

topic_id: the unique topic identifier assigned by a topic model
topic_label: a character vector of representative labels for topic_id
mean_gamma: the mean likelihood of topic_id across all documents