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我正在使用 twitter API 来生成情绪。我正在尝试根据推文生成词云。

这是我生成 wordcloud 的代码

wordcloud(clean.tweets, random.order=F,max.words=80, col=rainbow(50), scale=c(3.5,1))

结果：

在此处输入图像描述

我也试过这个：

pal <- brewer.pal(8,"Dark2")

wordcloud(clean.tweets,min.freq = 125,max.words = Inf,random.order  = TRUE,colors = pal)

结果：

在此处输入图像描述

我错过了什么吗？

这就是我获取和清理推文的方式：

#downloading tweets
tweets <- searchTwitter("#hanshtag",n = 5000, lang = "en",resultType = "recent")
# removing re tweets 
no_retweets <- strip_retweets(tweets , strip_manual = TRUE)

#converts to data frame
df <- do.call("rbind", lapply(no_retweets , as.data.frame))

#remove odd characters
df$text <- sapply(df$text,function(row) iconv(row, "latin1", "ASCII", sub="")) #remove emoticon
df$text = gsub("(f|ht)tp(s?)://(.*)[.][a-z]+", "", df$text) #remove URL
sample <- df$text


    # Cleaning Tweets 
    sum_txt1 <- gsub("(RT|via)((?:\\b\\w*@\\w+)+)","",sample)
    sum_txt2 <- gsub("http[^[:blank:]]+","",sum_txt1)
    sum_tx3 <- gsub("@\\w+","",sum_txt2)
    sum_tx4 <- gsub("[[:punct:]]"," ", sum_tx3)
    sum_tex5 <- gsub("[^[:alnum:]]", " ", sum_tx4)
    sum_tx6 <- gsub("RT  ","", sum_tex5)

    # WordCloud

    # data frame is not good for text convert it corpus
    corpus <- Corpus(VectorSource(sum_tx6))
    clean.tweets<- tm_map(corpus , content_transformer(tolower)) #converting everything to lower cases
    clean.tweets<- tm_map(guj_clean,removeWords, stopwords("english")) #stopword are words like of, the, a, as..
    clean.tweets<- tm_map(guj_clean, removeNumbers)
    clean.tweets<- tm_map(guj_clean, stripWhitespace)

提前致谢！

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1 回答 1

1

尝试将 wordcloud 上的比例从 c(3.5,1) 更改为 c(3.5,0.25)。

wordcloud(clean.tweets, random.order=F,max.words=80, col=rainbow(50), scale=c(3.5,0.25))
于 2017-12-09T04:58:18.377 回答