基本介紹

1. coreNLP

安裝package

packages = c("dplyr","ggplot2","rtweet" ,"xml2", "httr", "jsonlite", "data.tree", "NLP", "igraph","sentimentr","tidytext","wordcloud2","DiagrammeR","dplyr")
existing = as.character(installed.packages()[,1])
for(pkg in packages[!(packages %in% existing)]) install.packages(pkg)
library(wordcloud2)
library(ggplot2)
library(scales)
library(rtweet)
library(dplyr)
library(xml2)
library(httr)
library(jsonlite)
library(magrittr)
library(data.tree)
library(tidytext)
library(stringr)
library(DiagrammeR)
library(magrittr)
load("coreNLP_all.RData")

1.1 資料收集：tweets

(1). Twitter API設定 透過rtweet抓取tweets

app = 'Emotions COVID-19 Vaccine'
consumer_key = 'sldS3M1c37owWAxx88lRg8anU'
consumer_secret = 'lCsUtxqA6DWC9nW7xH2a5KAITLXEX8oj10tcWE7zRVTxgHARfC'
access_token = '1283052584312410112-LocNkHahyAJ50KR0sADTmHryO0k3Kq'
access_secret = 'gLssR17xxOZUDLeiF6sB5LiwSYAAVBE0mLjXQolINF4k3'
twitter_token <- create_token(app,consumer_key, consumer_secret,
                    access_token, access_secret,set_renv = FALSE)
#Consumer Keys:知道你的身分
#Authentication Tokens:認證給你的授權

(2). 設定關鍵字抓tweets

# 查詢關鍵字
key = c("#COVID-19")
context = "Vaccine"
q = paste(c(key,context),collapse=" AND ")   
# 查詢字詞 "#COVID-19 AND Vaccine"
# 為了避免只下#COVID-19 會找到非在Vaccine中的tweets，加入Vaccine要同時出現的條件

#抓10000筆 不抓轉推
tweets = search_tweets(q,lang="en",n=8000,include_rts = FALSE,token = twitter_token)

(3). tweets內容清理

## 用於資料清理
clean = function(txt) {
  txt = iconv(txt, "latin1", "ASCII", sub="") #改變字的encoding
  txt = gsub("(@|#)\\w+", "", txt) #去除@或#後有數字,字母,底線 (標記人名或hashtag)
  txt = gsub("(http|https)://.*", "", txt) #去除網址(.:任意字元，*:0次以上)
  txt = gsub("[ \t]{2,}", "", txt) #去除兩個以上空格或tab
  txt = gsub("\\n"," ",txt) #去除換行
  txt = gsub("\\s+"," ",txt) #去除一個或多個空格(+:一次以上)
  txt = gsub("^\\s+|\\s+$","",txt) #去除開頭/結尾有一個或多個空格
  txt = gsub("&.*;","",txt) #去除html特殊字元編碼
  txt = gsub("[^a-zA-Z0-9?!. ']","",txt) #除了字母,數字空白?!.的都去掉(表情符號去掉)
  txt }


tweets$text = clean(tweets$text)  #text套用資料清理

df = data.frame()
  
df = rbind(df,tweets)  # transfer to data frame

df = df[!duplicated(df[,"status_id"]),]  #去除重複的tweets
head(df)
## # A tibble: 6 x 90
##   user_id   status_id   created_at          screen_name  text            source 
##   <chr>     <chr>       <dttm>              <chr>        <chr>           <chr>  
## 1 49196776~ 1381131832~ 2021-04-11 06:27:43 Illuminatek~ Can you still ~ Twitte~
## 2 19050000  1381131829~ 2021-04-11 06:27:43 FinancialRe~ Australians co~ Echobox
## 3 12241562~ 1381131683~ 2021-04-11 06:27:08 KratikaTriv~ What is the po~ Twitte~
## 4 27987687  1381131650~ 2021-04-11 06:27:00 PinkNews     New HIV vaccin~ TweetD~
## 5 27987687  1380908932~ 2021-04-10 15:42:00 PinkNews     New HIV vaccin~ TweetD~
## 6 34713362  1381131563~ 2021-04-11 06:26:39 business     South Korea wi~ Social~
## # ... with 84 more variables: display_text_width <dbl>,
## #   reply_to_status_id <chr>, reply_to_user_id <chr>,
## #   reply_to_screen_name <chr>, is_quote <lgl>, is_retweet <lgl>,
## #   favorite_count <int>, retweet_count <int>, quote_count <int>,
## #   reply_count <int>, hashtags <list>, symbols <list>, urls_url <list>,
## #   urls_t.co <list>, urls_expanded_url <list>, media_url <list>,
## #   media_t.co <list>, media_expanded_url <list>, media_type <list>,
## #   ext_media_url <list>, ext_media_t.co <list>, ext_media_expanded_url <list>,
## #   ext_media_type <chr>, mentions_user_id <list>, mentions_screen_name <list>,
## #   lang <chr>, quoted_status_id <chr>, quoted_text <chr>,
## #   quoted_created_at <dttm>, quoted_source <chr>, quoted_favorite_count <int>,
## #   quoted_retweet_count <int>, quoted_user_id <chr>, quoted_screen_name <chr>,
## #   quoted_name <chr>, quoted_followers_count <int>,
## #   quoted_friends_count <int>, quoted_statuses_count <int>,
## #   quoted_location <chr>, quoted_description <chr>, quoted_verified <lgl>,
## #   retweet_status_id <chr>, retweet_text <chr>, retweet_created_at <dttm>,
## #   retweet_source <chr>, retweet_favorite_count <int>,
## #   retweet_retweet_count <int>, retweet_user_id <chr>,
## #   retweet_screen_name <chr>, retweet_name <chr>,
## #   retweet_followers_count <int>, retweet_friends_count <int>,
## #   retweet_statuses_count <int>, retweet_location <chr>,
## #   retweet_description <chr>, retweet_verified <lgl>, place_url <chr>,
## #   place_name <chr>, place_full_name <chr>, place_type <chr>, country <chr>,
## #   country_code <chr>, geo_coords <list>, coords_coords <list>,
## #   bbox_coords <list>, status_url <chr>, name <chr>, location <chr>,
## #   description <chr>, url <chr>, protected <lgl>, followers_count <int>,
## #   friends_count <int>, listed_count <int>, statuses_count <int>,
## #   favourites_count <int>, account_created_at <dttm>, verified <lgl>,
## #   profile_url <chr>, profile_expanded_url <chr>, account_lang <lgl>,
## #   profile_banner_url <chr>, profile_background_url <chr>,
## #   profile_image_url <chr>

df共有90個欄位，但我們在這裡僅會使用幾個欄位:

  • user_id: 用戶id
  • status_id : 推文id
  • created_at : 發文時間
  • text : 推文內容
  • source : 發文來源

了解資料的資料筆數以及時間分布

created_at已經是一個date類型的欄位，因此可以直接用min,max來看最遠或最近的日期
註:rtweet最多只能抓到距今10天的資料

nrow(df)
## [1] 7940
min(df$created_at)
## [1] "2021-04-10 15:32:41 UTC"
max(df$created_at)
## [1] "2021-04-11 06:27:43 UTC"

1-2串接CoreNLP API

(1). API呼叫的設定

server端 : + 需先在terminal開啟corenlp server + 在corenlp的路徑下開啟terminal輸入 java -mx4g -cp "*" edu.stanford.nlp.pipeline.StanfordCoreNLPServer -port 9000 -timeout 15000

# 產生coreNLP的api url，將本地端的網址轉成符合coreNLP服務的url
generate_API_url <- function(host, port="9000",
                    tokenize.whitespace="false", annotators=""){ #斷詞依據不是空格
    url <- sprintf('http://%s:%s/?properties={"tokenize.whitespace":"%s","annotators":"%s"}', host, port, tokenize.whitespace, annotators)
    url <- URLencode(url)
}
#指定服務的位置
host = "127.0.0.1"

generate_API_url(host)
# 呼叫coreNLP api
call_coreNLP <- function(server_host, text, host="localhost", language="eng",
                    tokenize.whitespace="true", ssplit.eolonly="true", annotators=c("tokenize","ssplit","pos","lemma","ner","parse","sentiment")){
  # 假設有兩個core-nlp server、一個負責英文（使用9000 port）、另一個則負責中文（使用9001 port）
  port <- ifelse(language=="eng", 9000, 9001);
  # 產生api網址
  url <- generate_API_url(server_host, port=port,
                    tokenize.whitespace=tokenize.whitespace, annotators=paste0(annotators, collapse = ','))
  
  result <- POST(url, body = text, encode = "json")
  doc <- httr::content(result, "parsed","application/json",encoding = "UTF-8")
  return (doc)
}
#文件使用coreNLP服務
coreNLP <- function(data,host){
  # 依序將每個文件丟進core-nlp進行處理，每份文件的回傳結果為json格式
  # 在R中使用objects來儲存處理結果
  result <- apply(data, 1 , function(x){
    object <- call_coreNLP(host, x['text'])
    list(doc=object, data=x)
  })
  
  return(result)
}

(2). 資料整理function

從回傳的object中整理斷詞出結果，輸出為 tidydata 格式

coreNLP_tokens_parser <- function(coreNLP_objects){
  
  result <- do.call(rbind, lapply(coreNLP_objects, function(obj){
    original_data <- obj$data
    doc <- obj$doc
    # for a sentences
    sentences <- doc$sentences
   
    sen <- sentences[[1]]
    
    tokens <- do.call(rbind, lapply(sen$tokens, function(x){
      result <- data.frame(word=x$word, lemma=x$lemma, pos=x$pos, ner=x$ner)
      result
    }))
    
    tokens <- original_data %>%
      t() %>% 
      data.frame() %>% 
      select(-text) %>% 
      slice(rep(1:n(), each = nrow(tokens))) %>% 
      bind_cols(tokens)
    
    tokens
  }))
  return(result)
}

從回傳的core-nlp object中整理出詞彙依存關係，輸出為 tidydata 格式

coreNLP_dependency_parser <- function(coreNLP_objects){
  result <- do.call(rbind, lapply(coreNLP_objects, function(obj){
    original_data <- obj$data
    doc <- obj$doc
    # for a sentences
    sentences <- doc$sentences
    sen <- sentences[[1]]
    dependencies <- do.call(rbind, lapply(sen$basicDependencies, function(x){
      result <- data.frame(dep=x$dep, governor=x$governor, governorGloss=x$governorGloss, dependent=x$dependent, dependentGloss=x$dependentGloss)
      result
    }))
  
    dependencies <- original_data %>%
      t() %>% 
      data.frame() %>% 
      select(-text) %>% 
      slice(rep(1:n(), each = nrow(dependencies))) %>% 
      bind_cols(dependencies)
    dependencies
  }))
  return(result)
}

從回傳的core-nlp object中整理出語句情緒，輸出為 tidydata 格式

coreNLP_sentiment_parser <- function(coreNLP_objects){
  result <- do.call(rbind, lapply(coreNLP_objects, function(obj){
    original_data <- obj$data
    doc <- obj$doc
    # for a sentences
    sentences <- doc$sentences
    sen <- sentences[[1]]
    
    sentiment <- original_data %>%
      t() %>% 
      data.frame() %>% 
      bind_cols(data.frame(sentiment=sen$sentiment, sentimentValue=sen$sentimentValue))
  
    sentiment
  }))
  return(result)
}

圖形化 Dependency tree

程式參考來源：https://stackoverflow.com/questions/35496560/how-to-convert-corenlp-generated-parse-tree-into-data-tree-r-package

# 圖形化顯示dependency結果
parse2tree <- function(ptext) {
  stopifnot(require(NLP) && require(igraph))
  
  # this step modifies coreNLP parse tree to mimic openNLP parse tree
  ptext <- gsub("[\r\n]", "", ptext)
  ptext <- gsub("ROOT", "TOP", ptext)


  ## Replace words with unique versions
  ms <- gregexpr("[^() ]+", ptext)                                      # just ignoring spaces and brackets?
  words <- regmatches(ptext, ms)[[1]]                                   # just words
  regmatches(ptext, ms) <- list(paste0(words, seq.int(length(words))))  # add id to words
  
  ## Going to construct an edgelist and pass that to igraph
  ## allocate here since we know the size (number of nodes - 1) and -1 more to exclude 'TOP'
  edgelist <- matrix('', nrow=length(words)-2, ncol=2)
  
  ## Function to fill in edgelist in place
  edgemaker <- (function() {
    i <- 0                                       # row counter
    g <- function(node) {                        # the recursive function
      if (inherits(node, "Tree")) {            # only recurse subtrees
        if ((val <- node$value) != 'TOP1') { # skip 'TOP' node (added '1' above)
          for (child in node$children) {
            childval <- if(inherits(child, "Tree")) child$value else child
            i <<- i+1
            edgelist[i,1:2] <<- c(val, childval)
          }
        }
        invisible(lapply(node$children, g))
      }
    }
  })()
  
  ## Create the edgelist from the parse tree
  edgemaker(Tree_parse(ptext))
  tree <- FromDataFrameNetwork(as.data.frame(edgelist))
  return (tree)
}

將句子丟入服務

取得coreNLP回傳的物件
先不要跑這段，會花大概半小時（如果你記憶體只有4G可能會當掉）

#gc() #釋放不使用的記憶體

#t0 = Sys.time()
#obj = df[,c(2,5)]  %>% filter(text != "") %>% coreNLP(host) 
#丟入本地執行 丟入coreNLP的物件 必須符合: 是一個data.frame 有一個text欄位

#Sys.time() - t0 #執行時間
#Time difference of 30 mins
#save.image("coreNLP_covid19.RData")
#先將會用到的東西存下來，要用可直接載RData
#tokens =  coreNLP_tokens_parser(obj)
#dependencies = coreNLP_dependency_parser(obj)
#sentiment = coreNLP_sentiment_parser(obj)
#save.image("coreNLP_all.RData")

1-3 提取結果

(1). 斷詞、詞彙還原、詞性標註、NER

tokens =  coreNLP_tokens_parser(obj)
head(tokens,20)
##              status_id         word        lemma pos ner
## 1  1381131832515817472          Can          can  MD   O
## 2  1381131832515817472          you          you PRP   O
## 3  1381131832515817472        still        still  RB   O
## 4  1381131832515817472        catch        catch  VB   O
## 5  1381131832515817472      Covid19      covid19  NN   O
## 6  1381131832515817472        after        after  IN   O
## 7  1381131832515817472      getting          get VBG   O
## 8  1381131832515817472          the          the  DT   O
## 9  1381131832515817472     vaccine?     vaccine?  NN   O
## 10 1381131832515817472         YES!         yes!  NN   O
## 11 1381131832515817472           It           it PRP   O
## 12 1381131832515817472           is           be VBZ   O
## 13 1381131832515817472     possible     possible  JJ   O
## 14 1381131832515817472           to           to  TO   O
## 15 1381131832515817472        still        still  RB   O
## 16 1381131832515817472        catch        catch  VB   O
## 17 1381131832515817472        Covid        Covid NNP   O
## 18 1381131832515817472        after        after  IN   O
## 19 1381131832515817472 vaccination. vaccination. NNP   O
## 20 1381131832515817472     Supposed      suppose VBN   O
  • coreNLP_tokens_parser欄位:
    • status_id : 對應原本df裡的status_id，為一則tweets的唯一id
    • word: 原始斷詞
    • lemma : 對斷詞做詞形還原
    • pos : part-of-speech,詞性
    • ner: 命名實體

(2). 命名實體標註(NER)

  • 從NER查看特定類型的實體，辨識出哪幾種類型
unique(tokens$ner)
##  [1] "O"                 "MISC"              "ORDINAL"          
##  [4] "DATE"              "CAUSE_OF_DEATH"    "COUNTRY"          
##  [7] "NUMBER"            "DURATION"          "ORGANIZATION"     
## [10] "TITLE"             "CITY"              "NATIONALITY"      
## [13] "STATE_OR_PROVINCE" "PERSON"            "TIME"             
## [16] "LOCATION"          "SET"               "PERCENT"          
## [19] "RELIGION"          "IDEOLOGY"          "CRIMINAL_CHARGE"  
## [22] "MONEY"             "URL"
#除去entity為Other，有多少種word有被標註entity
length(unique(tokens$word[tokens$ner != "O"])) 
## [1] 4440

(3). 轉小寫

因為大小寫也會影響corenlp對NER的判斷，因此我們一開始給的推文內容是沒有處理大小寫的，但在跑完anotator後，為了正確計算詞頻，創建新欄位lower_word與lower_lemma，存放轉換小寫的word與lemma。轉成小寫的目的是要將不同大小寫的同一字詞（如Evergiven與evergiven）都換成小寫，再來計算詞頻

tokens$lower_word = tolower(tokens$word)
tokens$lower_lemma = tolower(tokens$lemma)

1.4 探索分析 - NER

涉及到的國家(COUNTRY)

我們可以透過coreNLP中的NER解析出在Twitter上面談論covid19疫苗的事情，所涉及到的國家(COUNTRY)，以初步了解這個議題的主要國家。

tokens %>%
  filter(ner == "COUNTRY") %>%  #篩選NER為COUNTRY
  group_by(lower_word) %>% #根據word分組
  summarize(count = n()) %>% #計算每組
  top_n(n = 13, count) %>%
  ungroup() %>% 
  mutate(word = reorder(lower_word, count)) %>%
  ggplot(aes(word, count)) + 
  geom_col()+
  ggtitle("Word Frequency (NER is COUNTRY)") +
  theme(text=element_text(size=14))+
  coord_flip()

  • 討論最多的國家是india
涉及到的組織(ORGANIZATION)

我們可以透過coreNLP中的NER解析出在Twitter上面談論covid19疫苗的事情，所涉及到的組織(ORGANIZATION)，以初步了解這個議題的主要公司/單位。

tokens %>%
  filter(ner == "ORGANIZATION") %>%  #篩選NER為ORGANIZATION
  group_by(lower_word) %>% #根據word分組
  summarize(count = n()) %>% #計算每組
  top_n(n = 10, count) %>%
  ungroup() %>% 
  mutate(word = reorder(lower_word, count)) %>%
  ggplot(aes(word, count)) + 
  geom_col()+
  ggtitle("Word Frequency (NER is ORGANIZATION)") +
  theme(text=element_text(size=14))+
  coord_flip()

  • pfizer 輝瑞公司，是一家總部位於美國紐約的跨國製藥、生物技術公司，研發總部位於康乃狄克州的格羅頓市。根據收入排名，輝瑞是全球最大的製藥公司。
  • walgreensduane reade 杜安里德 (Duane Reade)是Walgreens Boots Alliance旗下的一家連鎖藥店和便利店。
  • cvs CVS藥局是美國的一家藥妝店連鎖企業，也是美國最大的藥妝店連鎖企業， “covid19”
涉及到的人物(PERSON)

我們可以透過coreNLP中的NER解析出在Twitter上面談論covid19疫苗的事情，所涉及到的人物(PERSON)，以初步了解這個議題的主要人物。

tokens %>%
  filter(ner == "PERSON") %>%  #篩選NER為PERSON
  group_by(lower_word) %>% #根據word分組
  summarize(count = n()) %>% #計算每組
  top_n(n = 10, count) %>%
  ungroup() %>% 
  mutate(word = reorder(lower_word, count)) %>%
  ggplot(aes(word, count)) + 
  geom_col()+
  ggtitle("Word Frequency (NER is PERSON)") +
  theme(text=element_text(size=14))+
  coord_flip()

  • Johnson & Johnson

1.5 探索分析 - Dependency

語句依存關係結果
dependencies = coreNLP_dependency_parser(obj)
head(dependencies,20)
##              status_id       dep governor governorGloss dependent
## 1  1381131832515817472      ROOT        0          ROOT         4
## 2  1381131832515817472       aux        4         catch         1
## 3  1381131832515817472     nsubj        4         catch         2
## 4  1381131832515817472    advmod        4         catch         3
## 5  1381131832515817472       obj        4         catch         5
## 6  1381131832515817472      mark        7       getting         6
## 7  1381131832515817472     advcl        4         catch         7
## 8  1381131832515817472       det       10          YES!         8
## 9  1381131832515817472  compound       10          YES!         9
## 10 1381131832515817472       obj        7       getting        10
## 11 1381131832515817472     nsubj       13      possible        11
## 12 1381131832515817472       cop       13      possible        12
## 13 1381131832515817472 acl:relcl       10          YES!        13
## 14 1381131832515817472      mark       16         catch        14
## 15 1381131832515817472    advmod       16         catch        15
## 16 1381131832515817472     xcomp       13      possible        16
## 17 1381131832515817472       obj       16         catch        17
## 18 1381131832515817472      case       19  vaccination.        18
## 19 1381131832515817472       obl       16         catch        19
## 20 1381131832515817472       acl       19  vaccination.        20
##    dependentGloss
## 1           catch
## 2             Can
## 3             you
## 4           still
## 5         Covid19
## 6           after
## 7         getting
## 8             the
## 9        vaccine?
## 10           YES!
## 11             It
## 12             is
## 13       possible
## 14             to
## 15          still
## 16          catch
## 17          Covid
## 18          after
## 19   vaccination.
## 20       Supposed
視覺化 Dependency tree
parse_tree <- obj[[113]]$doc[[1]][[1]]$parse
tree <- parse2tree(parse_tree)
SetNodeStyle(tree, style = "filled,rounded", shape = "box")
plot(tree)

1.6 探索分析 - Sentiment

語句情緒值

情緒分數從最低分0~最高分4
+ 0,1 : very negative,negative
+ 2 : neutral
+ 3,4 : very positive,postive

sentiment = coreNLP_sentiment_parser(obj)
head(sentiment,20)
##              status_id
## 1  1381131832515817472
## 2  1381131829978271749
## 3  1381131683269775360
## 4  1381131650017411074
## 5  1380908932525330433
## 6  1381131563396653059
## 7  1381131492282224643
## 8  1381131377819713537
## 9  1381131361226997760
## 10 1381130703497093122
## 11 1380941752756887555
## 12 1381130898272231424
## 13 1381131348954415104
## 14 1381130611386015744
## 15 1380964456415555594
## 16 1381131038743801856
## 17 1381096556607180804
## 18 1381131288116072454
## 19 1380922421084045313
## 20 1380921654734434306
##                                                                                                                                                                                                                                                                  text
## 1  Can you still catch Covid19 after getting the vaccine? YES! It is possible to still catch Covid after vaccination. Supposed to be unlikely but I know someone who has. So as the county reopens maintain distancing stay safe. If we are carefulno more lockdowns.
## 2                                                                                 Australians could receive at least their first COVID19 vaccine injection by Christmas with the federal government working towards the new goal under the reworked national rollout.
## 3                                                                                                                                                 What is the point in getting vaccinated when they are completely ineffective against this new wave of corona virus?
## 4                                                                                                                                                                          New HIV vaccine 'based on Moderna's COVID jab' shows huge promise after first human trials
## 5                                                                                                                                                                          New HIV vaccine 'based on Moderna's COVID jab' shows huge promise after first human trials
## 6                                                                                                                                                          South Korea will resume AstraZeneca's Covid19 vaccine inoculations for those between the ages of 30 and 60
## 7                                                                                                                                                                                                                                     10042021 Covid19 vaccine jabbed
## 8               Data through April 1 not an April Fool's Day Joke! They've already reported 2342 deaths in the United States from the COVID19 vaccine experimental drug plus 941 patients with permanent disability from the shot and 1484 life threatening injuries.
## 9                                                                                                                                                                                                                Vaccination appointments are available at CVS.DRACUT
## 10                                                                Mu mus can anyone tell why should one take a vaccine after having a covid19 infection  recovered? Vaccine is a simulation why don't you open a driving school for those who have permanent license.
## 11                                                                                                                                                                                   Congratulations your vaccine immunity lasts lower or equal to Covid19 infection.
## 12                                                                Mu mus can anyone tell why should one take a vaccine after having a covid19 infection  recovered? Vaccine is a simulation why don't you open a driving school for those who have permanent license.
## 13                                                                Mu mus can anyone tell why should one take a vaccine after having a covid19 infection  recovered? Vaccine is a simulation why don't you open a driving school for those who have permanent license.
## 14                                                                Mu mus can anyone tell why should one take a vaccine after having a covid19 infection  recovered? Vaccine is a simulation why don't you open a driving school for those who have permanent license.
## 15                                                                                                                                                         Vaccine appointments available at Walgreens Saint Louis from Apr 14 to Apr 15. Sign up here zip code 63113
## 16                                                                                                                                                            Vaccine appointments available at Walgreens Jennings from Apr 12 to Apr 15. Sign up here zip code 63136
## 17                                                                                                                                                                                                    Vaccine appointments available at CVS Saint Louis. Sign up here
## 18                                                                                                                                                         Vaccine appointments available at Walgreens Saint Louis from Apr 13 to Apr 15. Sign up here zip code 63121
## 19                                                                                                                                                         Vaccine appointments available at Walgreens Saint Louis from Apr 11 to Apr 15. Sign up here zip code 63136
## 20                                                                                                                                                                     Vaccine appointments available at Walgreens Saint Louis on Apr 15. Sign up here zip code 63108
##    sentiment sentimentValue
## 1    Neutral              2
## 2    Neutral              2
## 3    Neutral              2
## 4   Positive              3
## 5   Positive              3
## 6    Neutral              2
## 7    Neutral              2
## 8   Positive              3
## 9    Neutral              2
## 10   Neutral              2
## 11   Neutral              2
## 12   Neutral              2
## 13   Neutral              2
## 14   Neutral              2
## 15   Neutral              2
## 16   Neutral              2
## 17   Neutral              2
## 18   Neutral              2
## 19   Neutral              2
## 20   Neutral              2
資料集中的情緒種類
unique(sentiment$sentiment)
## [1] "Neutral"      "Positive"     "Negative"     "Verynegative" "Verypositive"
sentiment$sentimentValue = sentiment$sentimentValue %>% as.numeric
#了解情緒文章的分佈
sentiment$sentiment %>% table()
## .
##     Negative      Neutral     Positive Verynegative Verypositive 
##         2014         4887          958            5            6
平均情緒分數時間趨勢
df$date = as.Date(df$created_at)

sentiment %>% 
  merge(df[,c("status_id","source","date")]) %>%
  group_by(date) %>% 
  summarise(avg_sentiment = mean(sentimentValue,na.rm=T)) %>% 
  ggplot(aes(x=date,y=avg_sentiment)) + 
  geom_line()

不同用戶端情緒時間趨勢
sentiment %>% 
  merge(df[,c("status_id","source","date")]) %>%
  filter(source %in% c("Twitter Web Client","Twitter for iPhone","Twitter for Android")) %>% 
  group_by(date,source) %>% 
  summarise(avg_sentiment = mean(sentimentValue,na.rm=T)) %>% 
  ggplot(aes(x=date,y=avg_sentiment,color=source)) + 
  geom_line()
## `summarise()` has grouped output by 'date'. You can override using the `.groups` argument.

了解情緒分佈，以及在正面情緒及負面情緒下，所使用的文章詞彙為何?
#了解正面文章的詞彙使用
sentiment %>% 
  merge(tokens) %>% 
  anti_join(stop_words) %>% 
  filter(!lower_word %in% c('i','the','covid19')) %>% 
  filter(sentiment == "Verypositive" | sentiment =='Positive') %>%
  group_by(lower_lemma) %>% #根據lemma分組
  summarize(count = n()) %>% 
  filter(count >5 & count<400)%>%
  wordcloud2()
## Joining, by = "word"
#了解負面文章的詞彙使用
sentiment %>% 
  merge(tokens) %>% 
  anti_join(stop_words) %>% 
  filter(!lower_word %in% c('i','the')) %>% 
  filter(sentiment == "Verynegative" | sentiment =='Negative') %>%
  group_by(lower_lemma) %>% 
  summarize(count = n()) %>% 
  filter(count >10 &count<400)%>%
  wordcloud2()

“wordcloud”

2. Sentimentr 英文情緒分析

library(sentimentr)
## Warning: package 'sentimentr' was built under R version 4.0.5
mytext <- c(
    "I heard you on the phone, I had you in mind",
    "I've been on the run since you walked through the hallway",
    "Heard it all before, I don't even mind",
    "I'll do anything, pull me in and I'm far away"
)

mytext <- get_sentences(mytext) #物件，將character向量轉成list,list裡放著character向量(已斷句)
每個文本的情緒分數

情緒分數為-1~1之間，<0屬於負面，>0屬於正面，0屬於中性

sentiment_by(mytext) #document level
##    element_id word_count sd ave_sentiment
## 1:          1         11 NA             0
## 2:          2         11 NA             0
## 3:          3          8 NA             0
## 4:          4         10 NA             0
每個句子的情緒分數
sentiment(mytext) #sentence level
##    element_id sentence_id word_count sentiment
## 1:          1           1         11         0
## 2:          2           1         11         0
## 3:          3           1          8         0
## 4:          4           1         10         0
  • 回傳4個欄位的dataframe:
    • element_id – 第幾個文本
    • sentence_id – 該文本中的第幾個句子
    • word_count – 句子字數
    • sentiment – 句子的情緒分數

2.2 使用twitter資料實踐在sentimentr

計算tweet中屬於正面的字
set.seed(10)
mytext <- get_sentences(tweets$text) #將text轉成list of characters型態
x <- sample(tweets$text, 1000, replace = FALSE) #隨機取1000筆，取後不放回
sentiment_words <- extract_sentiment_terms(x) #抓取其中帶有情緒的字
sentiment_counts <- attributes(sentiment_words)$counts #計算出現次數
sentiment_counts[polarity > 0,]   #正面的字
##           words polarity  n
##   1:     please      1.0 21
##   2:       care      1.0 14
##   3:   approved      1.0 12
##   4:   efficacy      1.0  6
##   5: understand      1.0  5
##  ---                       
## 351:       pray      0.1  1
## 352:    veteran      0.1  1
## 353:   building      0.1  1
## 354:    develop      0.1  1
## 355:      build      0.1  1
計算tweet中屬於負面的字
sentiment_counts[polarity < 0,] %>% arrange(desc(n)) %>% top_n(10) #出現次數最多的負面字
## Selecting by n
##         words polarity  n
##  1:      shot    -0.40 32
##  2:  declined    -0.60 22
##  3:      fake    -0.75 22
##  4:     crack    -0.50 15
##  5:     virus    -0.50 15
##  6:      risk    -0.75 15
##  7:  shortage    -0.75 13
##  8: emergency    -0.75 13
##  9:   warning    -0.50 12
## 10:      stop    -0.40 10
## 11:     rocky    -1.00 10
highlight每個句子，判斷屬於正/負面
set.seed(12)
df%>%
    filter(status_id %in% sample(unique(status_id), 30)) %>% #隨機30筆貼文
    mutate(review = get_sentences(text)) %$% 
    sentiment_by(review, status_id) %>%
    highlight()
## Saved in C:\Users\ASUS-NB\AppData\Local\Temp\RtmpOWVqEp/polarity.html
## Opening C:\Users\ASUS-NB\AppData\Local\Temp\RtmpOWVqEp/polarity.html ...

2.3 用日期來了解情緒波動

code 參考 https://github.com/trinker/sentimentr

tweets$date = format(tweets$created_at,'%Y%m%d')

(out  = tweets  %>%  with(
    sentiment_by( #document level
        get_sentences(text), 
        list( date)
    )
))
plot(out)

2.4 用日期來了解不同用戶端情的緒波動

(out  = tweets %>% filter(source %in% c("Twitter Web Client","Twitter for iPhone","Twitter for Android")) %>%  with(
    sentiment_by(
        get_sentences(text), 
        list(source, date)
    )
))
plot(out)