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Wordcloud来自R中的数据表

sentiment-analysis data-visualization r

coolhand · 技术社区 · 7 年前

我有一个数据表,由积极和消极的单词联想组成。我想创建两个词云,一个是积极的词,一个是消极的词。

举例 sentiment_words 表:

          element_id    sentence_id   negative     positive
1115:          1        1115          limits       agree,available
1116:          1        1116          slow         strongly,agree
1117:          1        1117                       management
1118:          1        1118                                      
1119:          1        1119          concerns     strongly,agree,better,

library(wordcloud) 和 library(sentimentr)

例如,如何仅从“肯定”列中提取单词来创建wordcloud?我不确定如何解决每一行都有多个相关单词的问题(例如,“同意,可用”应被视为两个条目)

我在这方面做了不同的尝试 wordcloud() 功能,如 wordcloud(words = sentiment_words$positive, freq = 3, min.freq = 1, max.words = 200, random.order = FALSE, rot.per=0.35, colors=brewer.pal(8, "Dark2"))

编辑:我已经试过了 tidyverse 回答如下,我得到的结果是: words n <chr> <int> 1 " \"ability\"" 3 2 " \"ability\")" 1 3 " \"acceptable\")" 1 4 " \"accomplish\"" 1 5 " \"accomplished\")" 1 6 " \"accountability\"" 1 7 " \"accountability\")" 1 8 " \"accountable\"" 2 9 " \"accountable\")" 1

我试过多种不同的方法 gsub() 和 apply ) 和 c( 但是还没有找到任何有效的方法。结果是应该一起计算的单词被分别计算(例如,“可接受”和“可接受”是wordcloud中的两个不同单词)

情话

for (j in seq(sentiment_words)) {
  sentiment_words[[j]] <- gsub("character(0)", "", sentiment_words[[j]])
  sentiment_words[[j]] <- gsub('"', "", sentiment_words[[j]])
  sentiment_words[[j]] <- gsub("c\\(", "", sentiment_words[[j]])
  sentiment_words[[j]] <- gsub(" ", "", sentiment_words[[j]])
  sentiment_words[[j]] <- gsub("\\)", "", sentiment_words[[j]])  
}

我还必须过滤掉 count_words 功能。注意,它过滤“character(0)”而不是“character(0)”,因为我删除了上面的右括号

filter(!!var != "character(0") %>%

实现上述功能可以提供基于文本极性的最干净的wordcloud

2 回复 | 直到 7 年前

Maurits Evers 7 年前

tidyverse -应该让您开始的基于策略的方法。我同意尤兹先生的观点,因为我不完全清楚问题出在哪里。

data.frame 使用基于特定列中逗号分隔的单词的字数 var 您的源数据的 df .

library(tidyverse)
count_words <- function(df, var) {
    var <- enquo(var)
    df %>%
        separate_rows(!!var, sep = ",") %>%
        filter(!!var != "") %>%
        group_by(!!var) %>%
        summarise(n = n()) %>%
        rename(words = !!var)
}

然后,我们可以为 positive 和 negative

df.pos <- count_words(df, positive)
df.neg <- count_words(df, negative)

让我们检查一下房间 数据帧

df.pos
# A tibble: 5 x 2
  words          n
  <chr>      <int>
1 agree          3
2 available      1
3 better         1
4 management     1
5 strongly       2

df.neg
# A tibble: 3 x 2
  words        n
  <chr>    <int>
1 concerns     1
2 limits       1
3 slow         1

让我们画出云这个词

library(wordcloud)
wordcloud(words = df.pos$words, freq = df.pos$n, min.freq = 1,
          max.words = 200, random.order = FALSE, rot.per = 0.35,
          colors = brewer.pal(8, "Dark2"))

wordcloud(words = df.neg$words, freq = df.neg$n, min.freq = 1,
          max.words = 200, random.order = FALSE, rot.per = 0.35,
          colors = brewer.pal(8, "Dark2"))

Tyler Rinker DaniM 7 年前

多愁善感的 attributes(sentiment_words)$counts ).这个 documentation for extract_sentiment_terms shows examples https://github.com/trinker/sentimentr/blob/master/R/extract_sentiment_terms.R

library(sentimentr)
library(wordcloud)
library(data.table)

set.seed(10)
x <- get_sentences(sample(hu_liu_cannon_reviews[[2]], 1000, TRUE))
sentiment_words <- extract_sentiment_terms(x)

sentiment_counts <- attributes(sentiment_words)$counts
sentiment_counts[polarity > 0,]

par(mfrow = c(1, 3), mar = c(0, 0, 0, 0))
## Positive Words
with(
    sentiment_counts[polarity > 0,],
    wordcloud(words = words, freq = n, min.freq = 1,
          max.words = 200, random.order = FALSE, rot.per = 0.35,
          colors = brewer.pal(8, "Dark2"), scale = c(4.5, .75)
    )
)
mtext("Positive Words", side = 3, padj = 5)

## Negative Words
with(
    sentiment_counts[polarity < 0,],
    wordcloud(words = words, freq = n, min.freq = 1,
          max.words = 200, random.order = FALSE, rot.per = 0.35,
          colors = brewer.pal(8, "Dark2"), scale = c(4.5, 1)
    )
)
mtext("Negative Words", side = 3, padj = 5)

sentiment_counts[, 
    color := ifelse(polarity > 0, 'red', 
        ifelse(polarity < 0, 'blue', 'gray70')
    )]

## Together
with(
    sentiment_counts[polarity != 0,],
    wordcloud(words = words, freq = n, min.freq = 1,
          max.words = 200, random.order = FALSE, rot.per = 0.35,
          colors = color, ordered.colors = TRUE, scale = c(5, .75)
    )
)
mtext("Positive (red) & Negative (blue) Words", side = 3, padj = 5)