我正在尝试从网页 Analyzing the US elections with Facebook and R 重新创建以下统计程序。
我不得不偏离原始代码，以便使其工作，但我似乎无法克服以下错误：

> # Apply Dictionary
> fb_liwc <- dfm(corpus, dictionary=myDi .... [TRUNCATED]
Error in UseMethod("dfm") :
  no applicable method for 'dfm' applied to an object of class "c('VCorpus','Corpus')"

我需要做些什么来使 dfm 方法正确编译？
代码如下：

library(Rfacebook)
library(stringr)
library(reshape2)
library(tm)
library(quanteda)

#######################################################################################
# Request Access token (!!!!access token will only be valid for two hours!!!!)via:
# https://developers.facebook.com/tools/explorer/
# Requires Facebook Account
token <- "XXXX" # access token should be inserted here

#######################################################################################

# Request posts
# Simple Example: Hillary Clinton (posts for one day: 2017-07-07)
fb_page <- getPage(page= "889307941125736", token=token, since='2016/07/07',until='2016/07/08')
fb_page$order <- 1:nrow(fb_page)

# Function to download the comments
download.maybe <- function(i, refetch=FALSE, path=".")
{
  post <- getPost(post=fb_page$id[i], comments = TRUE, likes = TRUE, token=token)
  post1 <- as.data.frame(melt(post))
}

# Apply function to download comments
files <- data.frame(melt(lapply(fb_page$order, download.maybe)))

# Select only comments
files_c <- files[complete.cases(files$message),]

# Split ID to abstract POST_ID
files_c$id2 <- lapply(strsplit(as.character(files_c$id), "_"), "[", 1)
files_c$ch <- nchar(files_c$id2)
files_a <- files_c[ which(files_c$ch >12), ]

# Change column name
names(files_a)[11] <- "POST_ID"

# Define date
files_a$date <- lapply(strsplit(as.character(files_a$created_time), "T"), "[", 1)
files_a$date1 <- as.character(files_a$date)

# Dine identifier to count comments
files_a$tempID <- 1

#######################################################################################
# Clean Data
dat2 <- gsub("[^[:alnum:]///' ]", "", files_a$message)
dat2 <- data.frame(dat2)

dat3 <- gsub("([.-])|[[:punct:]]", " ", dat2$dat2)
dat3 <- data.frame(dat3)
dat4 <- iconv(dat3$dat3, "latin1", "ASCII", sub="")
dat4 <- data.frame(dat4)

dat5 <- gsub('[[:digit:]]+', '', dat4$dat4)
dat5 <- data.frame(dat5)

dat6 <- tolower(dat5$dat5)
dat6 <- data.frame(dat6)

dat7 <- gsub("'", " ", dat6$dat6)
dat7 <- data.frame(dat7)

dat8 <- gsub("/", " ", dat7$dat7)
dat8 <- data.frame(dat8)

#######################################################################################
# Steps to replace empty entries
# Function to replace blanks with missing NA
blank2na <- function(x)
{
  z <- gsub("\\s+", "", x)
  x[z==""] <- NA
  return(x)
}

# Replace blanks with 'NA'
dat10 <- data.frame(sapply(dat8, blank2na))
dat10 <- data.frame(dat10)

# Define the relevant column as numeric
dat12 <- as.numeric(dat10$dat8)
dat12 <- data.frame(dat12)

# Define function if entry is numeric(non-numeric)
f <- function(x) is.numeric(x) & !is.na(x)
dat14 <- f(dat12$dat12)
dat14 <- data.frame(dat14)

# Reverse definition of numeric/character
dat16 <- as.character(ifelse(dat14$dat14 == "FALSE", dat8$dat8, 1010101010101010))
dat16 <- data.frame(dat16)
dat16 <- as.character(dat16$dat16)

# Combine NA and real value !!!! Select a individual Term (here: "Hallo")!!!!
dat8 <- as.character(dat8$dat8)
dat17 <- ifelse(dat16 != 1010101010101010, "HALLO", dat8)
dat17 <- data.frame(dat17)

######################################################################################
# Define Corpus
dat17$ch <- nchar(as.character(dat17$dat17))
dat17$bb <- ifelse(dat17$ch<4, "HALLO", as.character(dat17$dat17))
dat18 <- as.data.frame(dat17$bb [grep("nchar", dat17$bb ) ])
dat17$dat17 <- as.character(dat17$dat17)
dat_r <- as.data.frame(dat17)
colnames(dat_r)[1] <- "dat_r"
dat_r$dat_r <- as.character(dat_r$dat_r)
corpus <- Corpus(VectorSource(dat_r$dat_r))

######################################################################################
# Load Dictionary (https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html#lexicon)
# Negative/Positive Words
hu.liu.pos=scan('~/Documents/Project/positive-words.txt', what='character', comment.char = ';')
hu.liu.neg=scan('~/Documents/Project/negative-words.txt', what='character', comment.char = ';')
# Optional: Add Words to List
pos.words=c(hu.liu.pos, 'like')
neg.words=c(hu.liu.neg, 'bad')

# Combine Dictionaries
myDict <- dictionary(list(positive = pos.words, negative = neg.words))

######################################################################################
# Apply Dictionary
fb_liwc <- dfm(corpus, dictionary=myDict)
fb1 <- as.data.frame(fb_liwc)

######################################################################################
# Combine Analysis Data and Original Data
ALL <- cbind(files_a, fb1)