我正在分析几千篇报纸文章的文本，我想建立一个问题词典（例如，医疗保健，税收，犯罪等）。每个词典条目由几个术语组成（例如，医生，护士，医院等）
作为一种诊断，我想看看哪些术语构成了每个字典类别的主体。
这段代码说明了我所处的位置。我已经找到了一种方法来分别打印每个字典条目的顶部特性，但我希望在最后有一个连贯的 Dataframe ，这样我就可以可视化了。

library(quanteda)
]# set path
path_data <- system.file("extdata/", package = "readtext")

# import csv file
dat_inaug <- read.csv(paste0(path_data, "/csv/inaugCorpus.csv"))
corp_inaug <- corpus(dat_inaug, text_field = "texts") 
  corp_inaug %>% 
tokens(., remove_punct = T) %>% 
  tokens_tolower() %>% 
  tokens_select(., pattern=stopwords("en"), selection="remove")->tok

#I have about eight or nine dictionaries 
dict<-dictionary(list(liberty=c("freedom", "free"), 
                      justice=c("justice", "law")))
#This producesa a dfm of all the individual terms making up the dictionary
tok %>% 
tokens_select(pattern=dict) %>% 
  dfm() %>% 
  topfeatures()
  
#This produces the top features just making up the 'justice' dictionary entry
tok %>% 
  tokens_select(pattern=dict['justice']) %>% 
  dfm() %>% 
  topfeatures()
#This gets me close to what I want, but I can't figure out how to collapse this now 
#to visualize which are the most frequent terms that are making up each dictionary category

dict %>% 
  map(., function(x) tokens_select(tok, pattern=x)) %>% 
  map(., dfm) %>% 
map(., topfeatures)