使用嵌套列表列方法和Purrr以及Tidytext :: Unnest_Tokens

Question

我有一个包含调查回复的数据框，每行代表一个不同的人。一栏 - “文字” - 是一个开放式的文字问题。我想使用Tidytext :: unnest_tokens，以便我按行进行文本分析，包括情感分数，字数等。

以下是此示例的简单数据框：

Satisfaction<-c ("Satisfied","Satisfied","Dissatisfied","Satisfied","Dissatisfied")
Text<-c("I'm very satisfied with the services", "Your service providers are always late which causes me a lot of frustration", "You should improve your staff training, service providers have bad customer service","Everything is great!","Service is bad")
Gender<-c("M","M","F","M","F")
df<-data.frame(Satisfaction,Text,Gender)

然后我将Text列变成了字符......

df$Text<-as.character(df$Text)

接下来，我按id列分组并嵌套数据帧。

df<-df%>%mutate(id=row_number())%>%group_by(id)%>%unnest_tokens(word,Text)%>%nest(-id)

得到这个似乎已经工作正常，但现在如何使用purrr :: map函数处理嵌套列表列“word”？例如，如果我想使用dplyr :: mutate创建一个新列，每行有字数？

另外，是否有更好的方法来嵌套数据框，以便只有“文本”列是嵌套列表？

Answer 1

我喜欢使用purrr::map来做modeling for different groups，但是对于你所说的，我认为你可以坚持使用直接的dplyr。

您可以像这样设置数据框：

library(dplyr)
library(tidytext)

Satisfaction <- c("Satisfied",
                  "Satisfied",
                  "Dissatisfied",
                  "Satisfied",
                  "Dissatisfied")

Text <- c("I'm very satisfied with the services",
          "Your service providers are always late which causes me a lot of frustration", 
          "You should improve your staff training, service providers have bad customer service",
          "Everything is great!",
          "Service is bad")

Gender <- c("M","M","F","M","F")

df <- data_frame(Satisfaction, Text, Gender)

tidy_df <- df %>% 
    mutate(id = row_number()) %>% 
    unnest_tokens(word, Text)

然后，例如，找到每行的单词数，你可以使用group_by和mutate。

tidy_df %>%
    group_by(id) %>%
    mutate(num_words = n()) %>%
    ungroup
#> # A tibble: 37 × 5
#>    Satisfaction Gender    id      word num_words
#>           <chr>  <chr> <int>     <chr>     <int>
#> 1     Satisfied      M     1       i'm         6
#> 2     Satisfied      M     1      very         6
#> 3     Satisfied      M     1 satisfied         6
#> 4     Satisfied      M     1      with         6
#> 5     Satisfied      M     1       the         6
#> 6     Satisfied      M     1  services         6
#> 7     Satisfied      M     2      your        13
#> 8     Satisfied      M     2   service        13
#> 9     Satisfied      M     2 providers        13
#> 10    Satisfied      M     2       are        13
#> # ... with 27 more rows

您可以通过实现内部联接来进行情绪分析;看看some examples here。