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我有一个data.frame有周数，week和文本评论，text。我想将该week变量视为我的分组变量并对其进行一些基本的文本分析（例如qdap::polarity）。一些评论文本有多个句子；但是，我只关心本周的“整体”极性。

如何在运行之前将多个文本转换链接在一起qdap::polarity并遵守其警告消息？我能够将转换与 - 链接在一起tm::tm_map-tm::tm_reduce有什么可比的qdap吗？qdap::polarity在运行和/或之前预处理/转换此文本的正确方法是什么qdap::sentSplit？

以下代码/可重现示例中的更多详细信息：

library(qdap)
library(tm)

df <- data.frame(week = c(1, 1, 1, 2, 2, 3, 4),
                 text = c("This is some text. It was bad. Not good.",
                          "Another review that was bad!",
                          "Great job, very helpful; more stuff here, but can't quite get it.",
                          "Short, poor, not good Dr. Jay, but just so-so. And some more text here.",
                          "Awesome job! This was a great review. Very helpful and thorough.",
                          "Not so great.",
                          "The 1st time Mr. Smith helped me was not good."),
                 stringsAsFactors = FALSE)

docs <- as.Corpus(df$text, df$week)

funs <- list(stripWhitespace,
             tolower,
             replace_ordinal,
             replace_number,
             replace_abbreviation)

# Is there a qdap function that does something similar to the next line?
# Or is there a way to pass this VCorpus / Corpus directly to qdap::polarity?
docs <- tm_map(docs, FUN = tm_reduce, tmFuns = funs)


# At the end of the day, I would like to get this type of output, but adhere to
# the warning message about running sentSplit. How should I pre-treat / cleanse
# these sentences, but keep the "week" grouping?
pol <- polarity(df$text, df$week)

## Not run:
# check_text(df$text)
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1 回答 1

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您可以sentSplit按照警告中的建议运行，如下所示：

df_split <- sentSplit(df, "text")
with(df_split, polarity(text, week))

##   week total.sentences total.words ave.polarity sd.polarity stan.mean.polarity
## 1    1               5          26       -0.138       0.710             -0.195
## 2    2               6          26        0.342       0.402              0.852
## 3    3               1           3       -0.577          NA                 NA
## 4    4               2          10        0.000       0.000                NaN

请注意，我在 github 上提供了一个突破性情绪包感测器，它在速度、功能和文档方面比qdap版本有所改进。这会在sentiment_by函数内部进行句子拆分。下面的脚本允许您安装包并使用它：

if (!require("pacman")) install.packages("pacman")
p_load_gh("trinker/sentimentr")

with(df, sentiment_by(text, week))

##    week word_count        sd ave_sentiment
## 1:    2         25 0.7562542    0.21086408
## 2:    1         26 1.1291541    0.05781106
## 3:    4         10        NA    0.00000000
## 4:    3          3        NA   -0.57735027
于 2015-12-02T03:28:58.653 回答