您好，我正在处理本教程中的最后一个示例：主题随时间变化的比例。 https://tm4ss.github.io/docs/Tutorial_6_Topic_Models.html
我使用此代码为数据运行它
library(readxl)
library(tm)
# Import text data
tweets <- read_xlsx("C:/R/data.xlsx")
textdata <- tweets$text
#Load in the library 'stringr' so we can use the str_replace_all function.
library('stringr')
#Remove URL's
textdata <- str_replace_all(textdata, "https://t.co/[a-z,A-Z,0-9]*","")
textdata <- gsub("@\\w+", " ", textdata) # Remove user names (all proper names if you're wise!)
textdata <- iconv(textdata, to = "ASCII", sub = " ") # Convert to basic ASCII text to avoid silly characters
textdata <- gsub("#\\w+", " ", textdata)
textdata <- gsub("http.+ |http.+$", " ", textdata) # Remove links
textdata <- gsub("[[:punct:]]", " ", textdata) # Remove punctuation
#Change all the text to lower case
textdata <- tolower(textdata)
#Remove Stopwords. "SMART" is in reference to english stopwords from the SMART information retrieval system and stopwords from other European Languages.
textdata <- tm::removeWords(x = textdata, c(stopwords(kind = "SMART")))
textdata <- gsub(" +", " ", textdata) # General spaces (should just do all whitespaces no?)
# Convert to tm corpus and use its API for some additional fun
corpus <- Corpus(VectorSource(textdata)) # Create corpus object
#Make a Document Term Matrix
dtm <- DocumentTermMatrix(corpus)
ui = unique(dtm$i)
dtm.new = dtm[ui,]
#Fixes this error: "Each row of the input matrix needs to contain at least one non-zero entry" See: https://stackoverflow.com/questions/13944252/remove-empty-documents-from-documenttermmatrix-in-r-topicmodels
#rowTotals <- apply(datatm , 1, sum) #Find the sum of words in each Document
#dtm.new <- datatm[rowTotals> 0, ]
library("ldatuning")
library("topicmodels")
k <- 7
ldaTopics <- LDA(dtm.new, method = "Gibbs", control=list(alpha = 0.1, seed = 77), k = k)
#####################################################
#topics by year
tmResult <- posterior(ldaTopics)
tmResult
theta <- tmResult$topics
dim(theta)
library(ggplot2)
terms(ldaTopics, 7)
tweets$decade <- paste0(substr(tweets$date2, 0, 3), "0")
topic_proportion_per_decade <- aggregate(theta, by = list(decade = tweets$decade), mean)
top5termsPerTopic <- terms(topicModel, 7)
topicNames <- apply(top5termsPerTopic, 2, paste, collapse=" ")
# set topic names to aggregated columns
colnames(topic_proportion_per_decade)[2:(K+1)] <- topicNames
# reshape data frame
vizDataFrame <- melt(topic_proportion_per_decade, id.vars = "decade")
# plot topic proportions per deacde as bar plot
require(pals)
ggplot(vizDataFrame, aes(x=decade, y=value, fill=variable)) +
geom_bar(stat = "identity") + ylab("proportion") +
scale_fill_manual(values = paste0(alphabet(20), "FF"), name = "decade") +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
这是输入数据的excel文件 https://www.mediafire.com/file/4w2hkgzzzaaax88/data.xlsx/file
使用聚合函数运行该行时出现错误，我无法确定聚合发生了什么，我创建了与tutoria相同的“ decade”变量，将其显示并看上去好的，theta变量也可以..我根据这篇文章修改了几次聚合函数 Error in aggregate.data.frame : arguments must have same length
但是仍然有相同的错误..请帮助
答案 0 :(得分：2)
我不确定您要使用该命令实现什么
topic_proportion_per_decade <- aggregate(theta, by = list(decade = tweets$decade), mean)
据我所知，您只能用十年来产生
tweets$decade <- paste0(substr(tweets$date2, 0, 3), "0")
table(tweets$decade)
2010
3481
从tweets到textdata的所有预处理过程中，都会产生一些空行。这是您的问题开始的地方。
带有新的空行的文本数据是您的corpus和dtm的基础。您可以通过以下行摆脱它们：
ui = unique(dtm$i)
dtm.new = dtm[ui,]
与此同时，您基本上要删除dtm中的空列，从而更改对象的长度。没有空单元格的新dtm是
然后是主题模型的新基础。当您尝试对两个不同长度的对象使用aggregate()时，这又困扰了您：tweets$decade，仍然是theta的3418的旧长度，它是由主题模型，该模型又基于dtm.new －请记住，行数较少的模型。
我建议首先在tweets中获得一个ID列。以后，您可以使用ID来查找以后会被预处理删除的文本，并匹配tweet$decade和theta的长度。
我重写了您的代码-试试看：
library(readxl)
library(tm)
# Import text data
tweets <- read_xlsx("data.xlsx")
## Include ID for later
tweets$ID <- 1:nrow(tweets)
textdata <- tweets$text
#Load in the library 'stringr' so we can use the str_replace_all function.
library('stringr')
#Remove URL's
textdata <- str_replace_all(textdata, "https://t.co/[a-z,A-Z,0-9]*","")
textdata <- gsub("@\\w+", " ", textdata) # Remove user names (all proper names if you're wise!)
textdata <- iconv(textdata, to = "ASCII", sub = " ") # Convert to basic ASCII text to avoid silly characters
textdata <- gsub("#\\w+", " ", textdata)
textdata <- gsub("http.+ |http.+$", " ", textdata) # Remove links
textdata <- gsub("[[:punct:]]", " ", textdata) # Remove punctuation
#Change all the text to lower case
textdata <- tolower(textdata)
#Remove Stopwords. "SMART" is in reference to english stopwords from the SMART information retrieval system and stopwords from other European Languages.
textdata <- tm::removeWords(x = textdata, c(stopwords(kind = "SMART")))
textdata <- gsub(" +", " ", textdata) # General spaces (should just do all whitespaces no?)
# Convert to tm corpus and use its API for some additional fun
corpus <- Corpus(VectorSource(textdata)) # Create corpus object
#Make a Document Term Matrix
dtm <- DocumentTermMatrix(corpus)
ui = unique(dtm$i)
dtm.new = dtm[ui,]
#Fixes this error: "Each row of the input matrix needs to contain at least one non-zero entry" See: https://stackoverflow.com/questions/13944252/remove-empty-documents-from-documenttermmatrix-in-r-topicmodels
#rowTotals <- apply(datatm , 1, sum) #Find the sum of words in each Document
#dtm.new <- datatm[rowTotals> 0, ]
library("ldatuning")
library("topicmodels")
k <- 7
ldaTopics <- LDA(dtm.new, method = "Gibbs", control=list(alpha = 0.1, seed = 77), k = k)
#####################################################
#topics by year
tmResult <- posterior(ldaTopics)
tmResult
theta <- tmResult$topics
dim(theta)
library(ggplot2)
terms(ldaTopics, 7)
id <- data.frame(ID = dtm.new$dimnames$Docs)
colnames(id) <- "ID"
tweets$decade <- paste0(substr(tweets$date2, 0, 3), "0")
tweets_new <- merge(id, tweets, by.x="ID", by.y = "ID", all.x = T)
topic_proportion_per_decade <- aggregate(theta, by = list(decade = tweets_new$decade), mean)