get_topic_trends: Model and summarize topic trends based on text2vec LDA
In manuelbickel/textility: Utility functions for text mining
View source: R/get_topic_trends.R
get_topic_trends R Documentation
Model and summarize topic trends based on text2vec LDA
Description
Model and summarize topic trends based on text2vec LDA
Usage
get_topic_trends(doc_topic_distr, top_terms, doc_ids_years,
loess_aicc_span = c(0.1, 0.9), sep_labels = "#",
topic_selection_qt = 0.95, topic_selection_top_n = 10,
topic_selection_pattern)
Arguments
topic_selection_pattern
manuelbickel/textility documentation built on Nov. 25, 2022, 9:07 p.m.
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View source: R/get_topic_trends.R
| get_topic_trends | R Documentation |
Model and summarize topic trends based on text2vec LDA
Description
Model and summarize topic trends based on text2vec LDA
Usage
get_topic_trends(doc_topic_distr, top_terms, doc_ids_years, loess_aicc_span = c(0.1, 0.9), sep_labels = "#", topic_selection_qt = 0.95, topic_selection_top_n = 10, topic_selection_pattern)
Arguments
topic_selection_pattern |
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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