How do I get output after applying a dictionary to a DFM?

Hey Stack Overflow peeps,

I've currently analysing some small text data suing a dictionary i constructed.

I've tried applying my dictionary to my DFM by using dfm_lookup function, but i cannot recieve any output.

Does anyone know whats wrong?

My dictionary is a 24 word dict.

My code is:

## **FIRST I GATHER DATA** ###

library(rvest)
library(data.table)


url <- "https://www.ft.dk/da/udvalg/udvalgene/sau/dokumenter/alle_spoergsmaal?startDate=20170101&endDate=20191130&pageSize=3067"

url2 <- "https://www.ft.dk/da/udvalg/udvalgene/sau/dokumenter/alle_spoergsmaal?committeeAbbreviation=SAU&startDate=20051031&endDate=20191031&pageSize=1000&totalNumberOfRecords=18656&pageNumber="

n <- 2:19 # definerer antal sider fra hjemmesiden

p <- read_html(url)

spm <- p %>% html_nodes(".highlighted+ .column-documents .column-documents__icon-text") %>% html_text(trim=T)

spmstillerforsøg <- p %>% html_nodes("td.column-documents.hidden-xs") %>% html_text(trim=T)


dato <- p %>% html_nodes("td[data-title='Dato']") %>% html_text(trim = T)


dfs <- list()
for (i in n) {
  if (i == 1){
    p <- read_html(url)
  } else {
    urlnew <- paste0(url2, i)
    p <- read_html(urlnew)

  }}

#links <- p %>% html_nodes(xpath= "//a[@class='column-documents__link']") %>% html_attr("href)

df <- data.frame(spm = spm,
                 spoerger = spmstillerforsøg, 
                 dato = dato,
                 #fulltext = NA,
                 stringsAsFactors = F)
dfs[[length(dfs) + 1]] <- df

#}

dfnew <- as.data.frame(rbindlist(dfs))

save(dfnew, file="rigtigdata.Rda")

## **THEN I CREATE MY DFM AND CREATE MY DICTIONARY AND TRY AND APPLY IT** 

library(quanteda)
mitcorpus <- corpus(dfnew, text_field = "spm")

texts <- tokens(df$spm, 
                what = "word",
                remove_numbers = T,
                remove_punct = T,
                remove_symbols = T,
                remove_separators = T,
                remove_hyphens = T,
                remove_url = T,
                verbose = T)

texts <- tokens_tolower(texts)
texts <- tokens_remove(texts, stopwords("danish"))

quanteda_options("language_stemmer" = "danish")
texts <- tokens_wordstem(texts)

# get actual dfm from tokens
txt.mat <- dfm(texts)

## **HERE I CREATE MY DICTIONARY **
ordbord <- dictionary(list(reorganisering = c("årsværker", "personaleændringer", "personale"),
                grænsekontrol = c("Grænsekontrol", "grænsekontrollen"),
                afskrivning_restancer = c("skatterestancer", "restanceinddrivelsesmyndighede"),
                leasing = c("leasingbiler", "leasing"),
                sambeskatning = c("sambeskatning"),
                afgifter = c("afgiften", "afgifter", "afgiftsstigning", "sukkerafgift", "tobaksafgift", "registreringsafgift", "PSO-afgift", "afgiftsstigning", "afgiftslettelser"),
                gældssanering = c("gældssanering"),
                indrivelse = c("skatteinddrivelse", "gældsindrivelse", "indrivelsen"),
                indsatsplaner = c("indsatsplaner"),
                henvendelser = c("henvendelser", "henvendelse", "henvendelsen"),
                EFI = c("EFI"),
                topskat = c("topskat", "topskattelettelser", "topskatten"),
                skattelettelser = c("skattelettelser"),
                udbytteskat = c("udbytteskat", "udbyttesagen", "udbytteskandalen"),
                boligbeskatning = c("boligskat", "ejendomsvurderinger", "boligbeskatning", "boligskatten", "grundværdi"),
                moms = c("moms", "momsfritagelse", "nulmoms"),
                EU = c("EU", "ECOFIN", "toldsystem", "toldsystemet"),
                virksomhedskat = c("selskabsskat", "virksomhedsskat"),
                arveafgift = c("arveafgift", "generationsskifte", "virksomhedsoverdragelse", "familieejede"),
                eksport = c("eksport", "bileksport"),
                kontrol = c("skattekontrol", "kontrol", "kontrollen"),
                merprovenue = c("merprovenue", "skattehuller"),
                fradrag = c("beskæftigelsesfradrag", "fradrag", "skattefradrag", "fradraget"),
                skatteunddragelse = c("sort", "skatteunddragelse")))

mindfmmedordbog <- dfm_lookup(tm, 
                  dictionary = ordbord)
mindfmmedordbog

This just returns: Document-feature matrix of: 3,067 documents, 24 features (99.3% sparse).

Any info on how to get further, escpecially how i find out how many times and when my words are being used.

Again, all help is much appreciated - i'm still learning.

Thx in advance !