我想进行文本挖掘分析，但遇到任何麻烦。
使用 dput()，我加载了文本的一小部分。
text<-structure(list(ID_C_REGCODES_CASH_VOUCHER = c(3941L, 3941L, 3941L,
3945L, 3945L, 3945L, 3945L, 3945L, 3945L, 3945L, 3953L, 3953L,
3953L, 3953L, 3953L, 3953L, 3960L, 3960L, 3960L, 3960L, 3960L,
3960L, 3967L, 3967L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), GOODS_NAME = structure(c(19L,
17L, 15L, 18L, 16L, 23L, 21L, 14L, 22L, 20L, 6L, 2L, 10L, 8L,
7L, 13L, 5L, 11L, 7L, 12L, 4L, 3L, 9L, 9L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L
), .Label = c("", "* 2108609 SLOB.Mayon.OLIVK.67% 400ml", "* 3014084 D.Dym.Spikachki DEREVEN.MINI 1kg",
"* 3398012 DD Kolb.SERV.OKHOTN in / to v / y0.35", "* 3426789 WH.The corn rav guava / yagn.d / CAT seed 85g",
"197 Onion 1 kg", "2013077 MAKFA Makar.RAKERS 450g", "2030918 MARIA TRADITIONAL Biscuit 180g",
"2049750 MAKFA Makar.SHIGHTS 450g", "3420159 LEBED.Mol.past.3,4-4,5% 900g",
"3491144 LIP.NAP.ICE TEA green yellow 0.5 liter", "6788 MAKFA Makar.perya 450g",
"809 Bananas 1kg", "FetaXa Cheese product 60% 400g (", "Lemons 55+",
"MAKFA Macaroni feathers like. in / with", "Napkins paper color 100pcs PL",
"Package \"Magnet\" white (Plastiktre)", "Pasta Makfa snail flow-pack 450 g.",
"SHEBEKINSKIE Macaroni Butterfly №40", "SOFT Cotton sticks 100 PE (BELL",
"TENDER AGE Cottage cheese 10", "TOBUS steering-wheel 0.5kg flow"
), class = "factor")), .Names = c("ID_C_REGCODES_CASH_VOUCHER",
"GOODS_NAME"), class = "data.frame", row.names = c(NA, -61L))
(NA是偶然的。)
正文是检查产品的名称。
我想将任何相似的名称归为一组。
例如。在这里我手动取 MAKFA makar(乌克兰名称)。我用
"root or key word MAKFA Makar" 找到了 7 行Pasta Makfa snail flow-pack 450 g.
MAKFA Macaroni feathers like. in / with
2013077 MAKFA Makar.RAKERS 450g
2013077 MAKFA Makar.RAKERS 450g
6788 MAKFA Makar.perya 450g
2049750 MAKFA Makar.SHIGHTS 450g
2049750 MAKFA Makar.SHIGHTS 450g
所有产品位置具有相同的词根。
MAKFA Makar 不能像
MFAMKR作为输出我想得到 Initially class
1 Pasta Makfa snail flow-pack 450 g. MAKFA Makar.
2 MAKFA Macaroni feathers like. in / with MAKFA Makar.
3 2013077 MAKFA Makar.RAKERS 450g MAKFA Makar.
4 2013077 MAKFA Makar.RAKERS 450g MAKFA Makar.
5 6788 MAKFA Makar.perya 450g MAKFA Makar.
6 2049750 MAKFA Makar.SHIGHTS 450g MAKFA Makar.
7 2049750 MAKFA Makar.SHIGHTS 450g MAKFA Makar.
8 * 3398012 DD Kolb.SERV.OKHOTN in / to v / y0.35 kolb
9 * 3014084 D.Dym.Spikachki DEREVEN.MINI 1kg Spikachki
10 809 Bananas 1kg Bananas
11 Lemons 55+ Lemons
12 Napkins paper color 100pcs PL Napkins paper
13 SOFT Cotton sticks 100 PE (BELL Cotton sticks
14 SHEBEKINSKIE Macaroni Butterfly №40 SHEBEKINSKIE Macaroni
15 * 3426789 WH.The corn rav guava / yagn.d / Cat SEED 85g CAT seed
16 FetaXa Cheese product 60% 400g ( Cheese
17 3491144 LIP.NAP.ICE TEA green yellow 0.5 liter TEA
18 2030918 MARIA TRADITIONAL Biscuit 180g Biscuit
19 197 Onion 1 kg Onion
20 TOBUSsteering-wheel 0.5kg flow steering-wheel
21 Package "Magnet" white (Plastiktre) Package (Plastiktre)
22 * 2108609 SLOB.Mayon.OLIVK.67% 400ml Mayon
23 TENDER AGE Cottage cheese 10 Cottage cheese
我如何按词根对产品进行分类？(相反，单词 Makar.Makfa、cheese 中存在相同的模式)
最佳答案
我认为您可以通过清理然后聚类您的文本来获得您想要的位置 - 这是一个初学者:
text <- text[1:24,]
library(quanteda)
library(tidyverse)
hc <- text %>%
pull(GOODS_NAME) %>%
as.character %>%
quanteda::tokens(
remove_numbers = T,
remove_punct = T,
remove_symbols = T,
remove_separators = T
) %>%
quanteda::tokens_tolower() %>%
quanteda::tokens_remove(valuetype="regex", pattern = c("^\\d.*")) %>%
quanteda::dfm() %>%
textstat_simil(method = "jaccard") %>%
magrittr::multiply_by(-1) %>%
`attr<-`("Labels", text$GOODS_NAME) %>%
hclust(method = "average")
pdf(tf<-tempfile(fileext = ".pdf"), width = 20, height = 10)
plot(hc)
dev.off()
shell.exec(tf)
clusters <- cutree(hc, h = -0.1)
split(text, clusters)
关于r - 使用 R 对单词中的相同模式进行分类，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/52346232/