In Shusei-E/keyATM-OLD: Keyword Assisted Topic Models

knitr::opts_chunk$set(eval = T, echo = TRUE)

Preparing documents and covariates

Please read Preparation for the reading of documents and creating a list of keywords. We use bills data we prapared (documents and keywords).

keyATM takes matrix or data.frame (tibble) type covariates data. If you have $D$ documents and $M$ covariates, the matrix should be $D \times M$. In this example, we have a dummy variable that indicates party identification.

library(keyATM)
data(keyATM_data_bills)
bills_cov <- keyATM_data_bills$cov
dim(bills_cov)  # We have 140 documents and a single covariate

Please make sure that the order of covariates is the same as the order of documents.

library(quanteda)
bills_dfm <- keyATM_data_bills$doc_dfm  # quanteda object
keyATM_docs <- keyATM_read(bills_dfm)

bills_keywords <- list(
                       Education = c("education", "child", "student"),
                       Law       = c("court", "law", "attorney"),
                       Health    = c("public", "health", "program"),
                       Drug      = c("drug", "treatment")
                      )

Fitting the model

out <- keyATM(
              docs              = keyATM_docs,    # text input
              no_keyword_topics = 3,              # number of topics without keywords
              keywords          = bills_keywords, # keywords
              model             = "covariates",   # select the model
              model_settings    = list(covariates_data = bills_cov,
                                       covariates_formula = ~ RepParty),
              options           = list(seed = 50)
             )

The default number of iterations is 1500. Please check this page for available options.

Saving the model

Once you fit the model, you can save the model with save() for replication. This is the same as the Base model.

Checking top words

top_words(out)

Shusei-E/keyATM-OLD documentation built on Dec. 23, 2019, 6:42 p.m.