Question

我正在尝试关注情绪分析的在线教程。代码：

new_sentiments <- sentiments %>% #From the tidytext package
  filter(lexicon != "loughran") %>% #Remove the finance lexicon
  mutate( sentiment = ifelse(lexicon == "AFINN" & score >= 0, "positive",
                         ifelse(lexicon == "AFINN" & score < 0,
                                "negative", sentiment))) %>%
  group_by(lexicon) %>%
  mutate(words_in_lexicon = n_distinct(word)) %>%
  ungroup()

产生错误：

>Error in filter_impl(.data, quo) : 
>Evaluation error: object 'lexicon' not found.

有关，也许对我来说，“情感”表的行为很奇怪（损坏了吗？）。这是“情感”的标题：

> head(sentiments,3)
>  element_id sentence_id word_count sentiment                                  
> chapter
> 1          1           1          7         0 The First Book of Moses:  
> Called Genesis
> 2          2           1         NA         0 The First Book of Moses:  
> Called Genesis
> 3          3           1         NA         0 The First Book of Moses:  > 
> Called Genesis
>                                  category
> 1 The First Book of Moses:  Called Genesis
> 2 The First Book of Moses:  Called Genesis
> 3 The First Book of Moses:  Called Genesis

但是，如果我将Get_Sentiments用于bing，AFINN或NRC，则得到的响应看起来很合适：

>  get_sentiments("bing")
> # A tibble: 6,788 x 2
>   word        sentiment
>   <chr>       <chr>    >   1 2-faced     negative 
> 2 2-faces     negative 
> 3 a+          positive 
> 4 abnormal    negative

我试图删除（删除软件包）并重新安装tidytext；行为无变化。我正在运行R 3.5

即使我完全误解了问题，也希望任何人都能给我提供任何见解。

Answer 1

看来tidytext必须更改，这破坏了教程中的某些代码。

要使代码运行，请替换

new_sentiments <- sentiments %>% #From the tidytext package
  filter(lexicon != "loughran") %>% #Remove the finance lexicon
  mutate( sentiment = ifelse(lexicon == "AFINN" & score >= 0, "positive",
                              ifelse(lexicon == "AFINN" & score < 0,
                                     "negative", sentiment))) %>%
  group_by(lexicon) %>%
  mutate(words_in_lexicon = n_distinct(word)) %>%
  ungroup()

使用

new_sentiments <- get_sentiments("afinn")
names(new_sentiments)[names(new_sentiments) == 'value'] <- 'score'
new_sentiments <- new_sentiments %>% mutate(lexicon = "afinn", sentiment = ifelse(score >= 0, "positive", "negative"),
                                                     words_in_lexicon = n_distinct((word)))

接下来的几张图没有太大意义（因为我们现在仅使用一个词典），但是本教程的其余部分都可以使用

更新 here是tidytext软件包作者对所发生情况的很好解释。

Answer 2

以下说明将修复Data Camp tutorial中显示的new_sentiments数据集。

bing <- get_sentiments("bing") %>% 
     mutate(lexicon = "bing", 
            words_in_lexicon = n_distinct(word))    

nrc <- get_sentiments("nrc") %>% 
     mutate(lexicon = "nrc", 
            words_in_lexicon = n_distinct(word))

new_sentiments <- bind_rows(new_sentiments, bing, nrc)

接下来的说明将按照最初的意图显示“按词典统计的字数”表。

new_sentiments %>% 
     group_by(lexicon, sentiment, words_in_lexicon) %>% 
     summarise(distinct_words = n_distinct(word)) %>% 
     ungroup() %>% 
     spread(sentiment, distinct_words) %>% 
     mutate(lexicon = color_tile("lightblue", "lightblue")(lexicon), 
            words_in_lexicon = color_bar("lightpink")(words_in_lexicon)) %>% 
     my_kable_styling(caption = "Word Counts per Lexicon")

后续图形也将起作用！

Answer 3

我发现了类似的问题，我在下面尝试了此代码，希望对您有帮助

library(tm)
library(tidyr)
library(ggthemes)
library(ggplot2)
library(dplyr)
library(tidytext)
library(textdata)

# Choose the bing lexicon
get_sentiments("bing")
get_sentiments("afinn")
get_sentiments("nrc")

#define new
afinn=get_sentiments("afinn")
bing=get_sentiments("bing")
nrc=get_sentiments("nrc")

#check
head(afinn)
head(bing)
head(nrc)
head(sentiments) #from tidytext packages

#merging dataframe
merge_sentiments=rbind(sentiments,get_sentiments('bing'),get_sentiments('nrc'))
head(merge_sentiments) #check

merge2_sentiments=merge(merge_sentiments,afinn,by=1,all=T)
head(merge2_sentiments) #check

#make new data frame with column lexicon added
new_sentiments <- merge2_sentiments
new_sentiments <- new_sentiments %>% 
  mutate(lexicon=ifelse(sentiment=='positive','bing',ifelse(sentiment=='negative','bing',ifelse(sentiment=='NA','afinn','nrc'))))

colnames(new_sentiments)[colnames(new_sentiments)=='value']='score'

#check
head(new_sentiments)

情绪分析；找不到“词典”； “情感”被破坏了吗？

3 个答案: