Question

我正试图借助我的DTM（文档术语矩阵）或TDM（术语文档矩阵）在R中进行情感分析。我在论坛和Google上找不到任何类似的主题。因此，我创建了一个语料库，并从该语料库中在R中生成了dtm / tdm。下一步是应用情感分析，稍后需要通过SVM进行库存预测。我给的代码是：

    dtm <- DocumentTermMatrix(docs)
    dtm <- removeSparseTerms(dtm, 0.99)
    dtm <- as.data.frame(as.matrix(dtm))

    tdm <- TermDocumentMatrix(docs)
    tdm <- removeSparseTerms(tdm, 0.99)
    tdm <- as.data.frame(as.matrix(tdm))

我读到有可能在get_sentiments（）函数的帮助下通过tidytext包。但是不可能将其应用于DTM / TDM。我如何对已过滤，已词干化的已清除过滤词进行情绪分析？我看到很多人都对一个空洞句子进行了情感分析，但是我想将其应用到我的单个单词中，以查看它们是否是肯定的，否定的，得分等。

Answer 1

SentimentAnalysis与tm集成良好。

library(tm)
library(SentimentAnalysis)

documents <- c("Wow, I really like the new light sabers!",
               "That book was excellent.",
               "R is a fantastic language.",
               "The service in this restaurant was miserable.",
               "This is neither positive or negative.",
               "The waiter forget about my dessert -- what poor service!")

vc <- VCorpus(VectorSource(documents))
dtm <- DocumentTermMatrix(vc)

analyzeSentiment(dtm, 
  rules=list(
    "SentimentLM"=list(
      ruleSentiment, loadDictionaryLM()
    ),
    "SentimentQDAP"=list(
      ruleSentiment, loadDictionaryQDAP()
    )
  )
)
#   SentimentLM SentimentQDAP
# 1       0.000     0.1428571
# 2       0.000     0.0000000
# 3       0.000     0.0000000
# 4       0.000     0.0000000
# 5       0.000     0.0000000
# 6      -0.125    -0.2500000

Answer 2

要在dtm上使用tidytext来获取情感，请先将dtm转换为整齐的格式，然后在整洁的数据和极化词词典之间进行内部联接。我将使用与上面相同的文档。上面示例中的某些文档是肯定的，但给出了中立的分数。让我们看看tidytext的表现

library(tidytext)
library(tm)
library(dplyr)
library(tidyr)

documents <- c("Wow I really like the new light sabers",
           "That book was excellent",
           "R is a fantastic language",
           "The service in this restaurant was miserable",
           "This is neither positive or negative",
           "The waiter forget about my dessert -- what poor service")

# create tidy format
vectors <- as.character(documents)
v_source <- VectorSource(vectors)
corpuss <- VCorpus(v_source)
dtm <- DocumentTermMatrix(corpuss)
as_tidy <- tidy(dtm)

# Using bing lexicon: you can use other as well(nrc/afinn)
bing <- get_sentiments("bing")
as_bing_words <- inner_join(as_tidy,bing,by = c("term"="word"))
# check positive and negative words 
as_bing_words  

# set index for documents number 
index <- as_bing_words%>%mutate(doc=as.numeric(document))
# count by index and sentiment
index <- index %>% count(sentiment,doc)
# spread into positives and negavtives
index <- index %>% spread(sentiment,n,fill=0)
# add polarity scorer
index <- index %>% mutate(polarity = positive-negative)
index

Doc 4和6是负数，5表示中立，而其余部分是正数

使用TDM / DTM在R中进行情感分析

2 个答案: