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| as.tmCorpus | Create textmining Corpus |
| filter_documents | Function to filter tagged text |
| getDoc | Function to access documents for textmining objects |
| getMeta | Function to access meta data for textmining objects |
| make_tabled | Function to create tmWordCountsTable object from tmParsed |
| mallet_prepare | Helper function to use mallet topic modelling with tmCorpus |
| ngram | Function to create ngram docs |
| parse | Function to parse tmCorpus. As an outpus we have tmParsed object. |
| predict | predict for 'tmTopicModel' object |
| predict.jobjRef | predict for 'tmTopicModel' object |
| predict.LDA | predict for 'tmTopicModel' object |
| predict.tmTopicModel | predict for 'tmTopicModel' object |
| setDoc | Function to change documents for textmining objects |
| setMeta | Function to access meta data for textmining objects |
| tabler | Helper function for tabelarising documents |
| terms | Function to return the most frequent terms of tmTopicModels |
| terms.jobjRef | Function to return the most frequent terms of tmTopicModels |
| terms.tmTopicModel | Function to return the most frequent terms of tmTopicModels |
| tmCorpus | Function to create tmCorpus |
| tmMetaData | Function to create tmMetaData |
| tmParsed | Function to create tmParsed |
| tmTaggedCorpus | Function to create tmTaggedCorpus |
| tmTextDocument | Function to create single tmTextDocument with meta data. The object can store any from of documents: raw (string), parsed or table of words counts. |
| tmWordCountsTable | Function to create tmWordCountsTable |
| topic_network | Function to plot topic network |
| topic_table | Function to calculate topics and words arrays from the mallet model. |
| topic_wordcloud | Simple wordcloud visualization of the topics. |
| train | train for 'tmCorpus' object |
| train.DocumentTermMatrix | train for 'tmCorpus' object |
| train.tmCorpus | train for 'tmCorpus' object |