将 dfm 用于结构模型
Using a dfm for structural model
从这个过程中获得 dfm:
library(quanteda)
df <- data.frame(text = c("one text here", "one more here and there"))
toks_tweets <- tokens(df$text, remove_punct = TRUE)
dfmat_tweets <- dfm(toks_tweets,
stem = FALSE,
remove_punct = TRUE)
如何将它用于这样的结构建模:
library(stm)
fittedModel <- stm(documents = out$documents, vocab = out$vocab, K = 3, init.type = "Spectral")
您需要使用函数quanteda::convert
。该函数可以将不同包的dfm转换成不同的格式。请参阅 ?convert
了解所有选项。
请参阅下面的示例以了解您的示例的解决方案。
library(quanteda)
df <- data.frame(text = c("one text here", "one more here and there"), stringsAsFactors = FALSE)
toks_tweets <- tokens(df$text, remove_punct = TRUE)
dfmat_tweets <- dfm(toks_tweets,
stem = FALSE,
remove_punct = TRUE)
out <- convert(dfmat_tweets, to = "stm") # convert to stm format
library(stm)
fittedModel <- stm(documents = out$documents, vocab = out$vocab, K = 3, init.type = "Spectral")
fittedModel
# A topic model with 3 topics, 2 documents and a 6 word dictionary.
从这个过程中获得 dfm:
library(quanteda)
df <- data.frame(text = c("one text here", "one more here and there"))
toks_tweets <- tokens(df$text, remove_punct = TRUE)
dfmat_tweets <- dfm(toks_tweets,
stem = FALSE,
remove_punct = TRUE)
如何将它用于这样的结构建模:
library(stm)
fittedModel <- stm(documents = out$documents, vocab = out$vocab, K = 3, init.type = "Spectral")
您需要使用函数quanteda::convert
。该函数可以将不同包的dfm转换成不同的格式。请参阅 ?convert
了解所有选项。
请参阅下面的示例以了解您的示例的解决方案。
library(quanteda)
df <- data.frame(text = c("one text here", "one more here and there"), stringsAsFactors = FALSE)
toks_tweets <- tokens(df$text, remove_punct = TRUE)
dfmat_tweets <- dfm(toks_tweets,
stem = FALSE,
remove_punct = TRUE)
out <- convert(dfmat_tweets, to = "stm") # convert to stm format
library(stm)
fittedModel <- stm(documents = out$documents, vocab = out$vocab, K = 3, init.type = "Spectral")
fittedModel
# A topic model with 3 topics, 2 documents and a 6 word dictionary.