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我正在尝试使用 AFINN 字典（get_sentiments（“afinn”））对推文数据集的情绪。下面提供了数据集的示例： 

A tibble: 10 x 2
   Date                TweetText                                                
   <dttm>              <chr>                                                    
 1 2018-02-10 21:58:19 "RT @RealSirTomJones: Still got the moves! That was a lo~
 2 2018-02-10 21:58:19 "Yass Tom \U0001f600 #snakehips still got it #TheVoiceUK"
 3 2018-02-10 21:58:19 Yasss tom he’s some chanter #TheVoiceUK #ItsNotUnusual   
 4 2018-02-10 21:58:20 #TheVoiceUK SIR TOM JONES...HE'S STILL HOT... AMAZING VO~
 5 2018-02-10 21:58:21 I wonder how many hips Tom Jones has been through? #TheV~
 6 2018-02-10 21:58:21 Tom Jones has still got it!!! #TheVoiceUK                
 7 2018-02-10 21:58:21 Good grief Tom Jones is amazing #TheVoiceuk              
 8 2018-02-10 21:58:21 RT @tonysheps: Sir Thomas Jones you’re a bloody legend #~
 9 2018-02-10 21:58:22 @ITV Tom Jones what a legend!!! ❤️ #StillGotIt #TheVoice~
10 2018-02-10 21:58:22 "RT @RealSirTomJones: Still got the moves! That was a lo~

我想要做的是： 1. 将推文分成单独的单词。2. 使用 AFINN 词典对这些单词进行评分。3. 将每条推文的所有单词的得分相加 4. 将这个和返回到新的第三列，这样我就可以看到每条推文的得分。

对于类似的词典，我发现了以下代码：

# Initiate the scoreTopic
scoreTopic <- 0
# Start a loop over the documents
for (i in 1:length (myCorpus)) {
  # Store separate words in character vector
  terms <- unlist(strsplit(myCorpus[[i]]$content, " "))
  # Determine the number of positive matches
  pos_matches <- sum(terms %in% positive_words)
  # Determine the number of negative matches
  neg_matches <- sum(terms %in% negative_words)
  # Store the difference in the results vector
  scoreTopic [i] <- pos_matches - neg_matches
} # End of the for loop

dsMyTweets$score <- scoreTopic

但是，我无法调整此代码以使其与 afinn 字典一起使用。

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2 回答 2

3

这将是整理数据原则的一个很好的用例。让我们设置一些示例数据（这些是我的真实推文）。 

library(tidytext)
library(tidyverse)

tweets <- tribble(
    ~tweetID, ~TweetText,
    1, "Was Julie helping me because I don't know anything about Python package management? Yes, yes, she was.",
    2, "@darinself OMG, this is my favorite.",
    3, "@treycausey @ftrain THIS IS AMAZING.",
    4, "@nest No, no, not in error. Just the turkey!",
    5, "The @nest people should write a blog post about how many smoke alarms went off yesterday. (I know ours did.)")

现在我们有一些示例数据。在下面的代码中，unnest_tokens()对文本进行标记，即将其分解为单个单词（tidytext 包允许您对推文使用特殊的标记器）并inner_join()实现情绪分析。

tweet_sentiment <- tweets %>%
    unnest_tokens(word, TweetText, token = "tweets") %>%
    inner_join(get_sentiments("afinn"))
#> Joining, by = "word"

现在我们可以找到每条推文的分数。获取推文的原始数据集，然后left_join()获取sum()每条推文的分数。replace_na()tidyr的便捷功能可让您将结果NA值替换为零。

tweets %>%
    left_join(tweet_sentiment %>%
                  group_by(tweetID) %>%
                  summarise(score = sum(score))) %>%
    replace_na(list(score = 0))
#> Joining, by = "tweetID"
#> # A tibble: 5 x 3
#>   tweetID TweetText                                                  score
#>     <dbl> <chr>                                                      <dbl>
#> 1      1. Was Julie helping me because I don't know anything about …    4.
#> 2      2. @darinself OMG, this is my favorite.                          2.
#> 3      3. @treycausey @ftrain THIS IS AMAZING.                          4.
#> 4      4. @nest No, no, not in error. Just the turkey!                 -4.
#> 5      5. The @nest people should write a blog post about how many …    0.

由reprex 包(v0.2.0) 于 2018 年 5 月 9 日创建。

如果您对情感分析和文本挖掘感兴趣，我邀请您查看我们为 tidytext 提供的大量文档和教程。

于 2018-05-10T03:23:52.170 回答
0

备查：

Score_word <- function(x) {
  word_bool_vec <- get_sentiments("afinn")$word==x
  score <- get_sentiments("afinn")$score[word_bool_vec]
  return (score) }    

Score_tweet <- function(sentence) {
  words <- unlist(strsplit(sentence, " "))
  words <- as.vector(words)
  scores <- sapply(words, Score_word)
  scores <- unlist(scores)
  Score_tweet <- sum(scores)
  return (Score_tweet)
  }     

dsMyTweets$score<-apply(df, 1, Score_tweet)

这执行了我最初想要的！:)

于 2018-05-06T17:34:14.197 回答