统计错误+合并Tidytext中的bing情绪得分变量？

Question

我在对大量的文本进行情感分析。我在tidytext中使用bing词汇表来获得简单的二进制pos/neg分类，但想要计算文档中正字与总词(正和负)的比率。我对dplyr工作流感到生疏，但我想计算编码为“正”的单词数，并将其除以与情绪分类的单词总数。

我尝试了这种方法，使用了示例代码和备用数据。。。

library(tidyverse)
library(tidytext)

#Creating a fake tidytext corpus
df_tidytext <- data.frame(
  doc_id = c("Iraq_Report_2001", "Iraq_Report_2002"),
  text = c("xxxx", "xxxx") #Placeholder for text
)

#Creating a fake set of scored words with bing sentiments 
#for each doc in corpus
df_sentiment_bing <- data.frame(
  doc_id = c((rep("Iraq_Report_2001", each = 3)), 
             rep("Iraq_Report_2002", each = 3)),
  word = c("improve", "democratic", "violence",
           "sectarian", "conflict", "insurgency"),
  bing_sentiment = c("positive", "positive", "negative",
                "negative", "negative", "negative") #Stand-ins for sentiment classification
)

#Summarizing count of positive and negative words
# (number of positive words out of total scored words in each doc)
df_sentiment_scored <- df_tidytext %>%
  left_join(df_sentiment_bing) %>%
  group_by(doc_id) %>%
  count(bing_sentiment) %>%
  pivot_wider(names_from = bing_sentiment, values_from = n) %>%
  summarise(bing_score = count(positive)/(count(negative) + count(positive)))

但我得到了以下错误：

"Error: Problem with `summarise()` input `bing_score`.
x no applicable method for 'count' applied to an object of class "c('integer', 'numeric')"
ℹ Input `bing_score` is `count(positive)/(count(negative) + count(positive))`.
ℹ The error occurred in group 1: doc_id = "Iraq_Report_2001".

我想了解一下我在这里总结工作流程的错误之处。

Answer 1

如果列是数字的，我不明白在那里计数有什么意义。顺便说一句，这也是你犯错误的原因。

一种解决办法可以是：

#Summarizing count of positive and negative words
# (number of positive words out of total scored words in each doc)
 df_tidytext %>%
  left_join(df_sentiment_bing) %>%
  group_by(doc_id) %>%
  dplyr::count(bing_sentiment) %>%
  pivot_wider(names_from = bing_sentiment, values_from = n) %>%
  replace(is.na(.), 0) %>%
  summarise(bing_score = sum(positive)/(sum(negative) + sum(positive)))

您应该得到的结果是：

Joining, by = "doc_id"
# A tibble: 2 × 2
  doc_id           bing_score
  <fct>                 <dbl>
1 Iraq_Report_2001      0.667
2 Iraq_Report_2002      0