Applying Function to Multiple Files & Handling the resulting dataframes - r

Question

I've got a function which I'm trying to apply in a for loop that extracts a dataframe from multiple files and combines them into a single one.
This is how, from what I've read, I thought would be the best way to attack it but I get an empty list returned, when I was hoping for a list of dataframes which could be combined using bind_rows.
This is the code I'm using:
combined_functions <- function(file_name) {
#combines the get_dfm_df and get corp function: get dfm tibble straight from the file name
data_frame_returned<- get_dfm_df(getcorp(file_name))
data_frame_returned
}
list_of_dataframes <- list()
file.list <- dir(pattern ="DOCX$")
for (file in file.list) {
dataframe_of_file <- combined_function(file)
append(list_of_dataframes,dataframe_of_file)
}
bind_rows(list_of_dataframes, .id = "column_label") #https://stackoverflow.com/questions/2851327/convert-a-list-of-data-frames-into-one-data-frame
It creates an empty list, gets a list of the file names which the function combined_function uses to create a data frame out of the file and should, to my understanding, append this dataframe to the list. After all the files in the directory have been matched, bind_rows should combine it into one overall dataframe but it only returns an empty tibble. list_of_dataframes is also empty.
I've tried the solution in this answer but it didn't help:
Append a data frame to a list
https://www.dropbox.com/sh/z8vh50b370gcb1j/AAAcbnfAUOM6-y8uWn4-lUWLa?dl=0
This a link to the raw files I am using in this case, but I think the problem is a general one.
Appendix:
These are the functions combined_functions refer too. They work on the individual cases so I'm confident this is not the cause of the problem but I've included them for completeness anyway.
rm(list = ls())
library(quanteda)
library(quanteda.corpora)
library(readtext)
library(LexisNexisTools)
library(tidyverse)
library(tools)
getcorp<- function(file_name){
#function to take the lexis word document, convert it into quanteda corpus object, returns duplicate df and date from filename in list
LNToutput <- lnt_read(file_name)
duplicates_df <- lnt_similarity(LNToutput = LNToutput,
threshold = 0.99)
duplicates_df <- duplicates_df[duplicates_df$Similarity > 0.99] #https://github.com/JBGruber/LexisNexisTools creates dataframe of duplicate articles
LNToutput <- LNToutput[!LNToutput#meta$ID %in% duplicates_df$ID_duplicate, ] #removes these duplicates from the main dataframe
corp <- lnt_convert(LNToutput, to = "quanteda") #to return multiple values from the r function, must be placed in a list
corp_date_from_file_name <- basename(file_name)
file_date <- as.Date(corp_date_from_file_name, format ="%d_%m_%y")
list_of_returns <-list(duplicates_df, corp,file_date) #list returns has duplicate df in first position, corpus in second and the file date in third
list_of_returns
}
get_dfm_df <- function(corp_list){
# takes the corp from getcorp, applies lexicoder dictionary, adds the neg_pos etc to their equivalent columns,
# calculates the percentage each category is of the total number of sentiment bearing words, adds the date specified from the file name
corpus_we_want <- corp_list[[2]]
sentiment_df <- dfm(corpus_we_want, dictionary = data_dictionary_LSD2015) %>% #applies the dictionary
convert("data.frame") %>%
cbind(docvars(corpus_we_want)) %>% #https://stackoverflow.com/questions/60419692/how-to-convert-dfm-into-dataframe-but-keeping-docvars
as_tibble() %>%
mutate(combined_negative = negative + neg_positive, combined_positive = positive + neg_negative) %>%
mutate(pos_percentage = combined_positive/(combined_positive + combined_negative ), neg_percentage =combined_negative/(combined_positive + combined_negative ) ) %>%
mutate(date = corp_list[[3]])
sentiment_df
}