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使用 tm 库将文本与正面参考单词列表进行比较并返回正面单词出现次数的最佳方法是什么我希望能够返回参考文本中正面单词的总和。

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.")

这里有一些背景：

我要做的是计算积极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）

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2 回答 2

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这是另一种使用定制工具的方法，您可以在其中定义积极词词典并将其应用于任意数量的文本，以计算积极关键词。这使用quanteda包和dfm()方法来创建文档特征矩阵，并带有dictionary =参数。（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：这对示例来说可能并不重要，但是在示例文本中“伟大”的使用并不是一个积极的词。说明多义词和字典的危险。

于 2015-11-22T20:01:08.330 回答
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您可以在不使用tm包的情况下执行此操作。

尝试这个

contained <- lapply(positiveword_list, grepl, x = exampleText)

lapply返回一个列表。

出现的话：

>positiveword_list[contained == T]
"great" "great"
>length(contained[contained==T])
2

不存在的词：

>positiveword_list[contained == F]
"happy"    "fabulous"
>length(contained[contained==F])
2
于 2015-11-21T06:08:33.567 回答