我有一个变量"bio_sentences"，正如变量名称所暗示的那样，它有四到五个个体的生物句子(从"bio"变量中提取并拆分为句子(。我试图使用这种逻辑来确定一个人的性别......
Femalew <- c("She", "Her")
Check <- str_extract_all(bio,Femalew)
Check <- Check[Check != "character(0)"]
Gender <- vector("character")
if(length(Check) > 0){
Gender[1] <- "Female"
}else{
Gender[1] <- "Male"
}
for(i in 1:length(bio_sentences)){
Gender[i] <- Gender[1]
}
我得到了一个很好的结果(我的数据集中的大多数是男性(，但是尽管句子中有"她"或"她"，但很少有遗漏(一些女性没有被检测到(。无论如何，我可以提高逻辑的准确性或部署一些新功能，例如 grepl？
编辑：
data1.Gender A B C D E data1.Description
1 Female 0 0 0 0 0 Ranjit Singh President of Boparan Holdings Limited Ranjit is President of Boparan Holdings Limited.
2 Female 0 0 0 NA NA He founded the business in 1993 and has more than 25 years’ experience in the food industry.
3 Female 0 0 0 NA NA Ranjit is particularly skilled at growing businesses, both organically and through acquisition.
4 Female 0 0 0 NA NA Notable acquisitions include Northern Foods and Brookes Avana in 2011.
5 Female 0 0 0 NA NA Ranjit and his wife Baljinder Boparan are the sole shareholders of Boparan Holdings, the holding company for 2 Sisters Food Group.
6 Female 0 0 0 NA NA s
以上是来自数据的人，我的要求是代码读取"data1.description"中的所有行(在我的代码中这是在for循环中，因此它读取每个人的所有句子(，正如你所看到的，这个人是男性，其中一个句子中显然有一个"他"， 但是，通过应用我之前编写的上述逻辑，我将其视为"女性"。
正如@Merijn van Tilborg所说，你应该在脑海中非常清楚你的句子，因为如果有多个代词，你的工作就无法给出所需的输出。
但是，您也可以管理这些情况，我们可以尝试使用dplyr和tidytext包，但我们必须稍微清理一下数据：
# explicit the genders
female <- c("She", "Her")
male <- c("He", "His")
# here your data, with several examples of cases
df <- data.frame(
line = c(1,2,3,4,5,6),
text = c("She is happy", # female
"Her dog is happy", # female (if we look at the subject, it's not female..)
"He is happy", # male
"His dog is happy", # male
"It is happy", # ?
"She and he are happy"), # both!
stringsAsFactors = FALSE ) # life saver
现在我们可以尝试这样的事情：
library(tidytext)
library(dplyr)
df %>%
unnest_tokens(word, text) %>% # put words in rows
mutate(gender = ifelse(word %in% tolower(female),'female',
ifelse(word %in% tolower(male), 'male','unknown'))) %>% # detect male and female, remember tolower!
filter(gender!='unknown') %>% # remove the unknown
right_join(df) %>% # join with the original sentences keeping all of them
select(-word) # remove useless column
line gender text
1 1 female She is happy
2 2 female Her dog is happy
3 3 male He is happy
4 4 male His dog is happy
5 5 <NA> It is happy
6 6 female She and he are happy
7 6 male She and he are happy
你可以看到1,2,3,4个句子符合你的标准，"it"没有定义，如果有男性和女性，我们加倍行，让你明白为什么。
最后，您可以在一行中折叠，将以下内容添加到dplyr链中：
%>% group_by(text, line) %>% summarise(gender = paste(gender, collapse = ','))
# A tibble: 6 x 3
# Groups: text [?]
text line gender
<chr> <dbl> <chr>
1 He is happy 3 male
2 Her dog is happy 2 female
3 His dog is happy 4 male
4 It is happy 5 NA
5 She and he are happy 6 female,male
6 She is happy 1 female
编辑： 让我们尝试使用您的数据：
data1 <- read.table(text="
data1.Gender A B C D E data1.Description
1 Female 0 0 0 0 0 'Ranjit Singh President of Boparan Holdings Limited Ranjit is President of Boparan Holdings Limited.'
2 Female 0 0 0 NA NA 'He founded the business in 1993 and has more than 25 years’ experience in the food industry.'
3 Female 0 0 0 NA NA 'Ranjit is particularly skilled at growing businesses, both organically and through acquisition.'
4 Female 0 0 0 NA NA 'Notable acquisitions include Northern Foods and Brookes Avana in 2011.'
5 Female 0 0 0 NA NA 'Ranjit and his wife Baljinder Boparan are the sole shareholders of Boparan Holdings, the holding company for 2 Sisters Food Group.'
6 Female 0 0 0 NA NA 's'",stringsAsFactors = FALSE)
# explicit the genders, in this case I've put also the names
female <- c("She", "Her","Baljinder")
male <- c("He", "His","Ranjit")
# clean the data
df <- data.frame(
line = rownames(data1),
text = data1$data1.Description,
stringsAsFactors = FALSE)
library(tidytext)
library(dplyr)
df %>%
unnest_tokens(word, text) %>% # put words in rows
mutate(gender = ifelse(word %in% tolower(female),'female',
ifelse(word %in% tolower(male), 'male','unknown'))) %>% # detect male and female, remember tolower!
filter(gender!='unknown') %>% # remove the unknown
right_join(df) %>% # join with the original sentences keeping all of them
select(-word) %>%
group_by(text, line) %>%
summarise(gender = paste(gender, collapse = ','))
结果：
Joining, by = "line"
# A tibble: 6 x 3
# Groups: text [?]
text line gender
<chr> <chr> <chr>
1 He founded the business in 1993 and has more than 25 years’ ex~ 2 male
2 Notable acquisitions include Northern Foods and Brookes Avana ~ 4 NA
3 Ranjit and his wife Baljinder Boparan are the sole shareholder~ 5 male,male,fe~
4 Ranjit is particularly skilled at growing businesses, both org~ 3 male
5 Ranjit Singh President of Boparan Holdings Limited Ranjit is P~ 1 male,male
6 s 6 NA
真正的游戏是将所有你能想到的词定义为"男性"或"女性"。
这要复杂得多，因为上下文是这里的关键。看看下面的三个短语...
苏珊有一位伟大的教授，他的名字叫亚当。他教了他最喜欢的学生所有需要知道的东西...... (苏珊不是被检测为女性，而是被检测为男性(
苏珊有一位伟大的教授，他的名字叫亚当。他教了她所有要知道的事情... (好吧，我们现在有一个SHE和一个HE(
苏珊有一位伟大的教授，名叫亚当。亚当教了她所有要知道的事情... (好的，我们有一个 SHE(
除了已经给出的答案外，我还强烈建议将最常见的女性名字添加到该列表中。例如，她们可以很容易地在网上找到，作为一个国家/地区最受欢迎的 100 个女性名字。我敢肯定，即使你在女性名单上加上大约500个最常出现的名字，你也会得到一个相当不错的开始，对男性也是如此。
此外，我给你举一个例子，里面有一些决策规则。它是女性或男性的可能性有多大。一种方法可能是只计算出现次数并计算比率。根据比例，您可以做出自己的决定。我的选择只是一个任意的例子，每个决策一行(可以更有效地编码(。
library(data.table) ## just my personal preference above dplyr
library(stringr) ## just my personal favorite when I deal with strings
df = data.table(text = c("Because Sandra is a female name and we talk a few times about her, she is most likely a female he says.",
"Sandra is mentioned and the only references are about how she did everything to achieve her goals.",
"Nothing is mentioned that reveals a gender.",
"She talks about him and he talks about her.",
"Sandra says: he is nice and she is nice too.",
"Adam is a male and we only talk about him")))
f.indicators = c("she", "her", "susan", "sandra")
m.indicators = c("he", "him", "his", "steve", "adam")
df[, f.count := sum(str_split(str_to_lower(text), "[[:space:]]|[[:punct:]]")[[1]] %in% f.indicators, na.rm = TRUE), by = text]
df[, m.count := sum(str_split(str_to_lower(text), "[[:space:]]|[[:punct:]]")[[1]] %in% m.indicators, na.rm = TRUE), by = text]
df[f.count != 0 | m.count != 0, gender_ratio_female := f.count / (f.count + m.count)]
df[, decision := "Unknown"]
df[gender_ratio_female == 1, decision := "Female, no male indications"]
df[gender_ratio_female == 0, decision := "Male, no female indicators"]
df[gender_ratio_female > 0.4 & gender_ratio_female < 0.6, decision := "Gender should be checked"]
df[gender_ratio_female > 0.6 & gender_ratio_female < 1, decision := "Probably a Female"]
df[gender_ratio_female > 0 & gender_ratio_female < 0.4, decision := "Probably a Male"]
附言抱歉，我正在努力在此处格式化输出表，我是这里的新手
text f.count m.count gender_ratio_female decision
1: Because Sandra is a female name and we talk a few times about her, she is most likely a female he says. 3 1 0.7500 Probably a Female
2: Sandra is mentioned and the only references are about how she did everything to achieve her goals. 3 0 1.0000 Female, no male indications
3: Nothing is mentioned that reveals a gender. 0 0 NA Unknown
4: She talks about him and he talks about her. 2 2 0.5000 Gender should be checked
5: Sandra says: he is nice and she is nice too. 2 1 0.6667 Probably a Female
6: Adam is a male and we only talk about him 0 2 0.0000 Male, no female indicators