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r - R情绪分析；找不到“词典”；“感情”受损？

问题描述

我正在尝试遵循这个关于情绪分析的在线教程。编码：

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()

生成错误：

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

相关的，也许对我来说，“情绪”表的行为很奇怪（损坏了？）。这是“情绪”的负责人：

> 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，我会得到看起来合适的响应：

>  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

我尝试删除（remove.packages）并重新安装tidytext；行为没有改变。我正在运行 R 3.5

即使我完全误解了这个问题，我也会感谢任何人能给我的任何见解。

标签： rsentiment-analysis

解决方案

以下说明将修复new_sentiments数据集，如数据营教程中所示。

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))

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

new_sentiments <- bind_rows(bing, nrc, afinn)
names(new_sentiments)[names(new_sentiments) == 'value'] <- 'score'
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")

随后的图表也将起作用！

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