接着重複這篇文章
Data Visualization and Analysis of Taylor Swift’s Song Lyrics
情感分析
情感分析是啥暫時不太關注，主要關注文章裡的資料可視化部分，按照文章中的代碼準備資料
lyrics<-read.csv("taylor_swift_lyrics_1.csv",header=T)
lyrics_text <- lyrics$lyric
lyrics_text<- gsub('[[:punct:]]+', '', lyrics_text)
lyrics_text<- gsub("([[:alpha:]])\1+", "", lyrics_text)
library(syuzhet)
help(package="syuzhet")
ty_sentiment <- get_nrc_sentiment((lyrics_text))
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gsub
函數是用來替換字元的，基本的用法是
> gsub("A","a","AAAbbbccc")
[1] "aaabbbccc"
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第一個位置是要替換的字元，第二個位置是替換成啥，第三個位置是完整的字元串。
第一個位置應該是可以用正規表達式的，但是R語言的正規表達式自己還沒有掌握
是以下面兩行代碼
lyrics_text<- gsub('[[:punct:]]+', '', lyrics_text)
lyrics_text<- gsub("([[:alpha:]])\1+", "", lyrics_text)
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幹了啥自己還沒看明白，原文的英文注釋是
removing punctations and alphanumeric content
get_nrc_sentiment()
應該是做情感分析的函數吧？
暫時先不管他了
将
ty_sentiment <- get_nrc_sentiment((lyrics_text))
運作完得到了一個資料框
> head(ty_sentiment)
anger anticipation disgust fear joy sadness surprise trust
1 0 0 0 0 0 1 0 0
2 0 0 1 1 0 1 0 0
3 1 0 1 0 0 1 0 0
4 0 0 1 0 0 0 0 1
5 0 0 0 0 0 0 0 0
6 0 0 0 0 0 0 0 0
negative positive
1 0 0
2 1 0
3 1 0
4 1 0
5 0 0
6 0 0
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構造接下來作圖用到的資料集
sentimentscores<-data.frame(colSums(ty_sentiment[,]))
head(sentimentscores)
names(sentimentscores) <- "Score"
sentimentscores <- cbind("sentiment"=rownames(sentimentscores),sentimentscores)
rownames(sentimentscores) <- NULL
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最終的資料集長這個樣子
> sentimentscores
sentiment Score
1 anger 538
2 anticipation 760
3 disgust 427
4 fear 637
5 joy 956
6 sadness 684
7 surprise 429
8 trust 665
9 negative 1086
10 positive 1335
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ggplot2基本的柱形圖
library(ggplot2)
ggplot(data=sentimentscores,aes(x=sentiment,y=Score))+
geom_bar(aes(fill=sentiment),stat = "identity")+
theme(legend.position="none")+
xlab("Sentiments")+ylab("Scores")+
ggtitle("Total sentiment based on scores")+
theme_minimal()
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image.png
lyrics$lyric <- as.character(lyrics$lyric)
tidy_lyrics <- lyrics %>%
unnest_tokens(word,lyric)
song_wrd_count <- tidy_lyrics %>% count(track_title)
lyric_counts <- tidy_lyrics %>%
left_join(song_wrd_count, by = "track_title") %>%
rename(total_words=n)
lyric_sentiment <- tidy_lyrics %>%
inner_join(get_sentiments("nrc"),by="word")
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重複到這的時候遇到了報錯
是以下面自己構造資料集，可視化的結果就偏離真實情況了
lyric_counts$sentiment<-sample(colnames(ty_sentiment),
dim(lyric_counts)[1],
replace = T)
df<-lyric_counts%>%
count(word,sentiment,sort=T)%>%
group_by(sentiment)%>%
top_n(n=10)%>%
ungroup()
head(df)
head(lyric_counts)
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作圖
library(ggplot2)
png("1.png",height = 800,width = 1000)
ggplot(df,aes(x=reorder(word,n),y=n,fill=sentiment))+
geom_col(show.legend = F)+
facet_wrap(~sentiment,scales = "free")+
coord_flip()+
theme_bw()+
xlab("Sentiments") + ylab("Scores")+
ggtitle("Top words used to express emotions and sentiments")
dev.off()
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image.png
第二幅圖
df1<-lyric_counts%>%
count(track_title,sentiment,sort = T)%>%
group_by(sentiment)%>%
top_n(n=5)
png(file="2.png",width=1300,height=700)
ggplot(df1,aes(x=reorder(track_title,n),y=n,fill=sentiment))+
geom_bar(stat="identity",show.legend = F)+
facet_wrap(~sentiment,scales = "free")+
xlab("Sentiments") + ylab("Scores")+
ggtitle("Top songs associated with emotions and sentiments") +
coord_flip()+
theme_bw()
dev.off()
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image.png
接下來是最想重複的一幅圖
year_emotion<-lyric_counts%>%
count(sentiment,year)%>%
group_by(year,sentiment)%>%
summarise(sentiment_sum=sum(n))%>%
ungroup()
head(year_emotion)
year_emotion<-na.omit(year_emotion)
grid.col = c("2006" = "#E69F00", "2008" = "#56B4E9",
"2010" = "#009E73", "2012" = "#CC79A7",
"2014" = "#D55E00", "2017" = "#00D6C9",
"anger" = "grey", "anticipation" = "grey",
"disgust" = "grey", "fear" = "grey",
"joy" = "grey", "sadness" = "grey",
"surprise" = "grey", "trust" = "grey")
circos.par(gap.after = c(rep(6, length(unique(year_emotion[[1]])) - 1), 15,
rep(6, length(unique(year_emotion[[2]])) - 1), 15))
chordDiagram(year_emotion, grid.col = grid.col, transparency = .2)
title("Relationship between emotion and song's year of release")
circos.clear()
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image.png
重複過程中遇到很多dplyr包中的函數，都是第一次使用，抽時間在回過頭來看這些函數的用法！