R tm 包：如何将文本与正参考词列表和正词出现次数 return 进行比较
R tm Package: How to compare text to positive reference word list and return count of positive word occurrences
使用 tm 库比较文本与正面参考词列表和 return 正面词出现次数的最佳方法是什么我希望能够 return 正面词的总和在参考文本中。
问题：最好的方法是什么？
例如：
positiveword_list <- c("happy", "great", "fabulous", "great")
参考文字：
exampleText <- c("ON A BRIGHT SPRING DAY in the year 1677, “the good ship
Kent,” Captain Gregory Marlowe, Master, set sail from the great docks of London. She carried 230 English Quakers, outward bound for a new home in British North America. As the ship dropped down the Thames she was hailed by King Charles II, who happened to be sailing on the river. The two vessels made a striking contrast. The King’s yacht was sleek and proud in gleaming paintwork, with small cannons peeping through wreaths of gold leaf, a wooden unicorn prancing high above her prow, and the royal arms emblazoned upon her stern. She seemed to dance upon the water— new sails shining white in the sun, flags streaming bravely from her mastheads, officers in brilliant uniform, ladies in court costume, servants in livery, musicians playing, and spaniels yapping. At the center of attention was the saturnine figure of the King himself in all his regal splendor. On the other side of the river came the emigrant ship. She would have been bluff-bowed and round-sided, with dirty sails and a salt-stained hull, and a single ensign drooping from its halyard. Her bulwarks were lined with apprehensive passengers— some dressed in the rough gray homespun of the northern Pen-nines, others in the brown drab of London tradesmen, several in the blue suits of servant-apprentices, and a few in the tattered motley of the country poor.")
这是一些背景：
我想做的是计算正面作品的数量并将计数作为新列存储在数据框中。
count <- length(which(lapply(positiveword_list, grepl, x = exampleText]) == TRUE))
因此：
dataframeIn %>% mutate( posCount <- (length(which(lapply(positiveword_list, grepl, x = text) == TRUE))))
其中 text 是 dataFrameIn 中的一列（即 dataFrameIn$text）
您可以在不使用 tm 包的情况下执行此操作。
试试这个
contained <- lapply(positiveword_list, grepl, x = exampleText)
lapply returns 一个列表。
当前字数：
>positiveword_list[contained == T]
"great" "great"
>length(contained[contained==T])
2
不存在的单词：
>positiveword_list[contained == F]
"happy" "fabulous"
>length(contained[contained==F])
2
这是使用自定义工具的另一种方法，您可以在其中定义正面词词典并将其应用于任意数量的文本，以计算正面关键词。这使用 quanteda 包和 dfm() 方法创建一个文档特征矩阵，带有 dictionary = 参数。 （参见 ?dictionary。）
require(quanteda)
posDic <- dictionary(list(positive = positiveword_list))
myDfm <- dfm(exampleText, dictionary = posDic)
# Creating a dfm from a character vector ...
# ... lowercasing
# ... tokenizing
# ... indexing documents: 1 document
# ... indexing features: 157 feature types
# ... applying a dictionary consisting of 1 key
# ... created a 1 x 1 sparse dfm
# ... complete.
# Elapsed time: 0.014 seconds.
as.data.frame(myDfm)
# positive
# text1 1
# produces a data frame with the text and the positive count
cbind(text = exampleText, as.data.frame(myDfm))
注意：这可能对示例不重要，但示例文本中 "great" 的用法不是肯定词。说明多义词和字典的危险。
使用 tm 库比较文本与正面参考词列表和 return 正面词出现次数的最佳方法是什么我希望能够 return 正面词的总和在参考文本中。
问题：最好的方法是什么？
例如：
positiveword_list <- c("happy", "great", "fabulous", "great")
参考文字：
exampleText <- c("ON A BRIGHT SPRING DAY in the year 1677, “the good ship
Kent,” Captain Gregory Marlowe, Master, set sail from the great docks of London. She carried 230 English Quakers, outward bound for a new home in British North America. As the ship dropped down the Thames she was hailed by King Charles II, who happened to be sailing on the river. The two vessels made a striking contrast. The King’s yacht was sleek and proud in gleaming paintwork, with small cannons peeping through wreaths of gold leaf, a wooden unicorn prancing high above her prow, and the royal arms emblazoned upon her stern. She seemed to dance upon the water— new sails shining white in the sun, flags streaming bravely from her mastheads, officers in brilliant uniform, ladies in court costume, servants in livery, musicians playing, and spaniels yapping. At the center of attention was the saturnine figure of the King himself in all his regal splendor. On the other side of the river came the emigrant ship. She would have been bluff-bowed and round-sided, with dirty sails and a salt-stained hull, and a single ensign drooping from its halyard. Her bulwarks were lined with apprehensive passengers— some dressed in the rough gray homespun of the northern Pen-nines, others in the brown drab of London tradesmen, several in the blue suits of servant-apprentices, and a few in the tattered motley of the country poor.")
这是一些背景：
我想做的是计算正面作品的数量并将计数作为新列存储在数据框中。
count <- length(which(lapply(positiveword_list, grepl, x = exampleText]) == TRUE))
因此：
dataframeIn %>% mutate( posCount <- (length(which(lapply(positiveword_list, grepl, x = text) == TRUE))))
其中 text 是 dataFrameIn 中的一列（即 dataFrameIn$text）
您可以在不使用 tm 包的情况下执行此操作。
试试这个
contained <- lapply(positiveword_list, grepl, x = exampleText)
lapply returns 一个列表。
当前字数：
>positiveword_list[contained == T]
"great" "great"
>length(contained[contained==T])
2
不存在的单词：
>positiveword_list[contained == F]
"happy" "fabulous"
>length(contained[contained==F])
2
这是使用自定义工具的另一种方法，您可以在其中定义正面词词典并将其应用于任意数量的文本，以计算正面关键词。这使用 quanteda 包和 dfm() 方法创建一个文档特征矩阵，带有 dictionary = 参数。 （参见 ?dictionary。）
require(quanteda)
posDic <- dictionary(list(positive = positiveword_list))
myDfm <- dfm(exampleText, dictionary = posDic)
# Creating a dfm from a character vector ...
# ... lowercasing
# ... tokenizing
# ... indexing documents: 1 document
# ... indexing features: 157 feature types
# ... applying a dictionary consisting of 1 key
# ... created a 1 x 1 sparse dfm
# ... complete.
# Elapsed time: 0.014 seconds.
as.data.frame(myDfm)
# positive
# text1 1
# produces a data frame with the text and the positive count
cbind(text = exampleText, as.data.frame(myDfm))
注意：这可能对示例不重要，但示例文本中 "great" 的用法不是肯定词。说明多义词和字典的危险。