如何从 R 数据框中提取关键字

我是 R 中文本挖掘的新手。我想从数据框的列中删除停用词（即提取关键字）并将这些关键字放入新列中。

我尝试制作一个语料库，但它对我没有帮助。

df$C3是我目前拥有的。我想添加栏目df$C4，但我无法让它工作。

df <- structure(list(C3 = structure(c(3L, 4L, 1L, 7L, 6L, 9L, 5L, 8L, 
       10L, 2L), .Label = c("Are doing good", "For the help", "hello everyone", 
       "hope you all", "I Hope", "I need help", "In life", "It would work", 
       "On Text-Mining", "Thanks"), class = "factor"), C4 = structure(c(2L, 
       4L, 1L, 6L, 3L, 7L, 5L, 9L, 8L, 3L), .Label = c("doing good", 
       "everyone", "help", "hope", "Hope", "life", "Text-Mining", "Thanks", 
       "work"), class = "factor")), .Names = c("C3", "C4"), row.names = c(NA, 
       -10L), class = "data.frame")

head(df)
#               C3          C4
# 1 hello everyone    everyone
# 2   hope you all        hope
# 3 Are doing good  doing good
# 4        In life        life
# 5    I need help        help
# 6 On Text-Mining Text-Mining

该解决方案使用包dplyr and tidytext.

library(dplyr)
library(tidytext)

# subset of your dataset
dt = data.frame(C1 = c(108,20, 999, 52, 400),
                C2 = c(1,3,7, 6, 9),
                C3 = c("hello everyone","hope you all","Are doing good","in life","I need help"), stringsAsFactors = F)

# function to combine words (by pasting one next to the other)
f = function(x) { paste(x, collapse = " ") }

dt %>%
  unnest_tokens(word, C3) %>%      # split phrases into words
  filter(!word %in% stop_words$word) %>%   # keep appropriate words
  group_by(C1, C2) %>%             # for each combination of C1 and C2
  summarise(word = f(word)) %>%    # combine multiple words (if there are multiple)
  ungroup()                        # forget the grouping

# # A tibble: 2 x 3
#        C1    C2  word
#      <dbl> <dbl> <chr>
#   1    20     3  hope
#   2    52     6  life

这里的问题是该包中内置的“停用词”过滤掉了您想要保留的一些单词。因此，您必须添加一个手动步骤来指定需要包含的单词。你可以这样做：

dt %>%
  unnest_tokens(word, C3) %>%      # split phrases into words
  filter(!word %in% stop_words$word | word %in% c("everyone","doing","good")) %>%   # keep appropriate words
  group_by(C1, C2) %>%             # for each combination of C1 and C2
  summarise(word = f(word)) %>%    # combine multiple words (if there are multiple)
  ungroup()                        # forget the grouping

# # A tibble: 4 x 3
#        C1    C2       word
#      <dbl> <dbl>      <chr>
#   1    20     3       hope
#   2    52     6       life
#   3   108     1   everyone
#   4   999     7 doing good