我尝试运行lda主题建模
以下是Quanteda网站的示例：
require(quanteda)
require(quanteda.corpora)
require(lubridate)
require(topicmodels)
corp_news <- download('data_corpus_guardian')
corp_news_subset <- corpus_subset(corp_news, 'date' >= 2016)
ndoc(corp_news_subset)
dfmat_news <- dfm(corp_news, remove_punct = TRUE, remove = stopwords('en')) %>%
dfm_remove(c('*-time', '*-timeUpdated', 'GMT', 'BST')) %>%
dfm_trim(min_termfreq = 0.95, termfreq_type = "quantile",
max_docfreq = 0.1, docfreq_type = "prop")
我的数据具有以下格式：
dtext = data.frame(text = c("This dataset contains movie reviews along with their associated binary sentiment polarity labels. It is intended to serve as a benchmark for sentiment classification. This document outlines how the dataset was gathered, and how to use the files provided.", "The core dataset contains 50,000 reviews split evenly into 25k train and 25k test sets. The overall distribution of labels is balanced (25k pos and 25k neg). We also include an additional 50,000 unlabeled documents for unsupervised learning.", "There are two top-level directories [train/, test/] corresponding to the training and test sets. Each contains [pos/, neg/] directories for the reviews with binary labels positive and negative. Within these directories, reviews are stored in text files named following the convention [[id]_[rating].txt] where [id] is a unique id and [rating] is the star rating for that review on a 1-10 scale. For example, the file [test/pos/200_8.txt] is the text for a positive-labeled test set example with unique id 200 and star rating 8/10 from IMDb. The [train/unsup/] directory has 0 for all ratings because the ratings are omitted for this portion of the dataset."),stringsAsFactors = F)
如果我尝试将它们转换为语料库，则所有数据均作为一个文档生成。但是每一行对我来说都是一个文档。
我应该如何修改语料库，以使其在lda中每行读为一个文档？