# 6. Using tidytext with textmineR

#### Thomas W. Jones

#### 2021-06-27

# Using tidytext with textmineR

The `tidytext` package is one of the more popular natural language processing packages in R’s ecosystem. It follows conventions and syntax of the “tidyverse.”

You may prefer to use `tidytext` for a couple of reasons. First, `tidytext` has its own philosophy and syntax for handling text, particularly at early stages. You may be more familiar or comfortable with this approach. Second, `tidytext` does, theoretically, offer some more flexibility in options creating DTMs or TCMs. This early stage is critical to successful topic modeling.

See Text Mining with R: A Tidy Approach for more details about tidytext.

What follows is a short script combining `tidytext` with `textmineR`. Initial data curation and DTM creation is done with `tidytext`. Topic modeling is done with `textmineR` and the outputs are re-formatted in the flavor of `tidytext`’s “tidiers” for other topic models.

    ################################################################################
    # Example: Using tidytext with textmineR
    ################################################################################

    library(tidytext)
    library(textmineR)
    library(dplyr)
    #>
    #> Attaching package: 'dplyr'
    #> The following objects are masked from 'package:igraph':
    #>
    #>     as_data_frame, groups, union
    #> The following objects are masked from 'package:stats':
    #>
    #>     filter, lag
    #> The following objects are masked from 'package:base':
    #>
    #>     intersect, setdiff, setequal, union
    library(tidyr)
    #>
    #> Attaching package: 'tidyr'
    #> The following object is masked from 'package:igraph':
    #>
    #>     crossing
    #> The following objects are masked from 'package:Matrix':
    #>
    #>     expand, pack, unpack

    # load documents in a data frame
    docs <- textmineR::nih_sample

    # tokenize using tidytext's unnest_tokens
    tidy_docs <- docs %>%
      select(APPLICATION_ID, ABSTRACT_TEXT) %>%
      unnest_tokens(output = word,
                    input = ABSTRACT_TEXT,
                    stopwords = c(stopwords::stopwords("en"),
                                  stopwords::stopwords(source = "smart")),
                    token = "ngrams",
                    n_min = 1, n = 2) %>%
      count(APPLICATION_ID, word) %>%
      filter(n>1) #Filtering for words/bigrams per document, rather than per corpus

    tidy_docs <- tidy_docs %>% # filter words that are just numbers
      filter(! stringr::str_detect(tidy_docs$word, "^[0-9]+$"))

    # turn a tidy tbl into a sparse dgCMatrix for use in textmineR
    d <- tidy_docs %>%
      cast_sparse(APPLICATION_ID, word, n)

    # create a topic model
    m <- FitLdaModel(dtm = d,
                     k = 20,
                     iterations = 200,
                     burnin = 175)

    # below is equivalent to tidy_beta <- tidy(x = m, matrix = "beta")
    tidy_beta <- data.frame(topic = as.integer(stringr::str_replace_all(rownames(m$phi), "t_", "")),
                            m$phi,
                            stringsAsFactors = FALSE) %>%
      gather(term, beta, -topic) %>%
      tibble::as_tibble()

    # below is equivalent to tidy_gamma <- tidy(x = m, matrix = "gamma")
    tidy_gamma <- data.frame(document = rownames(m$theta),
                             m$theta,
                             stringsAsFactors = FALSE) %>%
      gather(topic, gamma, -document) %>%
      tibble::as_tibble()