Uber eat 停權 ptt

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系統參數設定
UberEats聲量趨勢圖
Foodpanda聲量趨勢圖
UberEats情緒趨勢圖
Foodpanda情緒趨勢圖

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系統參數設定

Sys.setlocale(category = "LC_ALL", locale = "zh_TW.UTF-8") # 避免中文亂碼

## Warning in Sys.setlocale(category = "LC_ALL", locale = "zh_TW.UTF-8"): 作業系統
## 回報無法實現設定語區為 "zh_TW.UTF-8" 的要求

## [1] ""

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
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library(stringr)
library(tidytext)
library(wordcloud2)
library(data.table)

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## The following objects are masked from 'package:dplyr':
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library(ggplot2)
library(reshape2)

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library(wordcloud)

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library(tidyr)

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library(readr)
library(scales)

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require(jiebaR)

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require(widyr)

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require(NLP)

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require(ggraph)

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require(igraph)

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###八卦版

gossip_data = fread('./gossip_article.csv',encoding = 'UTF-8')

過濾特殊字元

gossip_data = gossip_data %>% 
  filter(!grepl('_',word))

轉換日期格式

gossip_data$artDate = gossip_data$artDate %>% as.Date("%Y/%m/%d")

###資料整理-UberEats

UberEats = c("ubereats","ubereat","Ubereat","UBEREATS","UberEATS","uberEATS")
gossip_data$word[which(gossip_data$word %in% UberEats)] = "UberEats"

#找出有UberEats的文章網址
ubereats_url = gossip_data$artUrl[grepl("UberEats", gossip_data$word)]
#依據網址找出有UberEats文章
ubereats_data <- gossip_data %>% 
  filter(gossip_data$artUrl %in% ubereats_url)

###資料整理-Foodpanda

foodpanda = c("FoodPanda","FOODPANDA","Foodpanda","富胖達","food胖達","熊貓")
gossip_data$word[which(gossip_data$word %in%foodpanda)] = "foodpanda"

#找出有foodpanda的文章網址
foodpanda_url = gossip_data$artUrl[grepl("foodpanda", gossip_data$word)]
#依據網址找出有foodpanda文章
foodpanda_data <- gossip_data %>% 
  filter(gossip_data$artUrl %in% foodpanda_url)

###去除停用字

stop_words <- c("https", "com", "新聞", "完整", "沒有","有沒有","現在","八卦","jpg","imgur","news","http","www","udn","gif","內文","htm","ettoday","ETtoday","請問","蘇格蘭","網址","連結","記者","署名","來源","媒體","新聞標題","備註","表示","報導","今天","看到")

ubereats_data <- ubereats_data %>% 
  filter(!(ubereats_data$word %in% stop_words))

foodpanda_data <- foodpanda_data %>% 
  filter(!(foodpanda_data$word %in% stop_words))

###聲量比較為了觀察台灣武漢肺炎爆發前後的聲量和情緒，資料區間避開2019/10中外送員車禍事件的聲量高峰期。

UberEats聲量趨勢圖

ubereats_1031 <- ubereats_data %>% 
  filter(ubereats_data$artDate > as.Date("2019/10/31"))

ubereats_1031_day <- ubereats_1031 %>% 
  group_by(artDate) %>% 
  summarise(count = n()) %>% 
  arrange(desc(count))
ubereats_1031_day

## # A tibble: 77 x 2
##    artDate    count
##    <date>     <int>
##  1 2020-03-28   416
##  2 2019-11-08   302
##  3 2019-11-22   261
##  4 2020-04-18   244
##  5 2019-11-05   239
##  6 2019-11-11   218
##  7 2019-11-21   213
##  8 2020-04-12   210
##  9 2019-11-14   207
## 10 2020-04-15   195
## # ... with 67 more rows

由下面的趨勢圖可以看出，武漢肺炎爆發之後並沒有立即提高外送平台的討論聲量

day_1031_plot <- ubereats_1031_day %>% 
  ggplot(aes(x = artDate, y = count)) +
  geom_line(color = "purple", size = 1)  + 
  scale_x_date(labels = date_format("%Y/%m/%d")) +  
  geom_vline(xintercept = as.numeric(as.Date("2020-01-21"),col='red',size = 1))+
  ggtitle("ubereats 10/31後每日討論篇數") + 
  xlab("日期") + 
  ylab("數量") +
  theme(text = element_text(family = "Heiti TC Light"))
day_1031_plot

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前五多的文章日期

ubereats_1031_day %>% 
  top_n(5)

## Selecting by count

## # A tibble: 5 x 2
##   artDate    count
##   <date>     <int>
## 1 2020-03-28   416
## 2 2019-11-08   302
## 3 2019-11-22   261
## 4 2020-04-18   244
## 5 2019-11-05   239

ubereats_plot_top5 <- ubereats_1031 %>% 
  filter(artDate == as.Date("2019/11/08") | 
         artDate == as.Date("2019/11/22") | 
         artDate == as.Date("2020/03/28") |
         artDate == as.Date("2020/04/18") | 
         artDate == as.Date("2020/04/10")) %>% 
  group_by(artDate) %>% 
  top_n(5, count) %>% 
  ungroup() %>% 
  mutate(word = reorder(word, count)) %>%
  ggplot(aes(x=word, y=count, fill = artDate)) +
  geom_col(show.legend = FALSE) +
  labs(x = NULL, y = NULL) +
  facet_wrap(~artDate, scales="free", ncol = 3) + 
  coord_flip()
ubereats_plot_top5

ubereats_1031 %>% 
  filter(artDate == as.Date("2019/11/08") | 
         artDate == as.Date("2019/11/22") | 
         artDate == as.Date("2020/03/28") |
         artDate == as.Date("2020/04/10") | 
         artDate == as.Date("2020/04/18"))  %>% 
  distinct(artUrl, .keep_all = TRUE) %>% 
  select(artDate,artTitle,artUrl)

##      artDate                                     artTitle
## 1 2019-11-08  [新聞]50K主管「想要月薪10萬」賭氣離職當外送
## 2 2019-11-08 [新聞]消費者被剝皮？外送平台餐食費用多高於店
## 3 2019-11-22      [新聞]外送員車禍身亡1家罰500元1家罰不到
## 4 2019-11-22 [新聞]女大生重病在家超餓！外送員突按門鈴送餐
## 5 2020-03-28  [新聞]今年前二月機車肇事增加外送平台佔4.25%
## 6 2020-03-28   [新聞]寧夏夜市靠外送突圍下一步要走向社交電
## 7 2020-04-10    [新聞]5業者組外送國家隊救餐飲！UberEats、
## 8 2020-04-18                   [問卦]ubereats被盜刷四千元
## 9 2020-04-18               [新聞]外送員趴趴走憂成防疫破口
##                                                     artUrl
## 1 https://www.ptt.cc/bbs/Gossiping/M.1573204052.A.82C.html
## 2 https://www.ptt.cc/bbs/Gossiping/M.1573214540.A.679.html
## 3 https://www.ptt.cc/bbs/Gossiping/M.1574428391.A.B79.html
## 4 https://www.ptt.cc/bbs/Gossiping/M.1574445517.A.94B.html
## 5 https://www.ptt.cc/bbs/Gossiping/M.1585394042.A.C82.html
## 6 https://www.ptt.cc/bbs/Gossiping/M.1585410190.A.3CD.html
## 7 https://www.ptt.cc/bbs/Gossiping/M.1586514906.A.602.html
## 8 https://www.ptt.cc/bbs/Gossiping/M.1587171437.A.983.html
## 9 https://www.ptt.cc/bbs/Gossiping/M.1587215241.A.758.html

重點新聞摘要: [武漢肺炎爆發前] 1.「想要月薪10萬」賭氣離職當外送員 2.未針對每一餐廳的商品（餐食）定價，揭露「店內價」、「非店內價」的商品資訊，且經過比對，各平台的部份餐廳其外送商品（餐食）的標價，確有出現高於店內消費價格的情形。 3.國慶連假陸續有兩名餐飲外送員車禍身亡，迫使勞動部勞檢並認定兩名外送員分別與美食平台業者foodpanda、Uber Eats為雇傭關係，業者依法應為外送員投保勞保與就業保險。但勞保局昨表示，Uber Eats是外商，在台僅有稅籍編號，非勞保強制投保單位，所以無法開罰；另一家foodpanda則因該外送員僅到職兩天就身亡，試算後，也只能罰五百多元。

[武漢肺炎爆發後] 1.寧夏夜市靠外送突圍，下一步要走向社交電商:武漢肺炎疫情蔓延，寧夏夜市部分攤商業績卻在不景氣中增長3成，關鍵是外送平台是逆襲重要關鍵。不僅如此，也將與電商龍頭合作，估5月可正式走向網路平台。(寧夏夜市觀光協會理事長林定國) 2.今年一至二月機車肇事件數與死傷人數，較前三年增加，市長柯文哲當場質疑跟騎機車美食外送的外送員有關 3.經濟部祭2.65億補助1.2萬家餐飲及零售業者引入外送，昨5家本土業者組成國家隊，並自願將抽成砍半到15%甚至5%(utaway（賽米資訊）、有無外送、foodomo（專聯科技）將原本25%到35%抽成降到15%；inline則直接僅抽5%；全球快遞過去每趟運送費為100元，也降低到75元。)。但兩大外送龍頭Ubereats、Foodpanda不約而同都未入列，經濟部揭露原因表示Uber eats正在申請投資許可，而Foodpanda有申請但正在審查中，但也鼓勵國內外所有外送平台都願意降低抽成比例，一起組建外送國家隊。 4.陳時中表示，外送員及郵遞員確實有風險，如何做到不接觸又可以送達、簽收及取款是努力的方向，但仍不考慮對居家檢疫及隔離者做標注。

Foodpanda聲量趨勢圖

foodpanda_1031 <- foodpanda_data %>% 
  filter(foodpanda_data$artDate > as.Date("2019/10/31"))

foodpanda_1031_day <- foodpanda_1031 %>% 
  group_by(artDate) %>% 
  summarise(count = n()) %>% 
  arrange(desc(count))
foodpanda_1031_day

## # A tibble: 136 x 2
##    artDate    count
##    <date>     <int>
##  1 2020-01-15   859
##  2 2020-02-12   840
##  3 2020-01-16   706
##  4 2020-01-13   678
##  5 2020-01-18   668
##  6 2019-11-09   618
##  7 2019-12-05   552
##  8 2019-11-08   538
##  9 2020-04-18   470
## 10 2020-02-14   455
## # ... with 126 more rows

foodpanda_day_1031_plot <- foodpanda_1031_day %>% 
  ggplot(aes(x = artDate, y = count)) +
  geom_line(color = "purple", size = 1)  + 
  scale_x_date(labels = date_format("%Y/%m/%d")) +  
  geom_vline(xintercept = as.numeric(as.Date("2020-01-21"),col='red',size = 1))+
  ggtitle("Foodpanda 10/31後每日討論篇數") + 
  xlab("日期") + 
  ylab("數量") +
  theme(text = element_text(family = "Heiti TC Light"))
foodpanda_day_1031_plot

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前五多的文章日期

foodpanda_1031_day %>% 
  top_n(10)

## Selecting by count

## # A tibble: 10 x 2
##    artDate    count
##    <date>     <int>
##  1 2020-01-15   859
##  2 2020-02-12   840
##  3 2020-01-16   706
##  4 2020-01-13   678
##  5 2020-01-18   668
##  6 2019-11-09   618
##  7 2019-12-05   552
##  8 2019-11-08   538
##  9 2020-04-18   470
## 10 2020-02-14   455

foodpanda_plot_top5 <- foodpanda_1031 %>% 
  filter(artDate == as.Date("2019/11/08") | 
         artDate == as.Date("2019/12/05") | 
         artDate == as.Date("2020/01/15") |
         artDate == as.Date("2020/02/12") | 
         artDate == as.Date("2020/04/18")) %>% 
  group_by(artDate) %>% 
  top_n(10, count) %>% 
  ungroup() %>% 
  mutate(word = reorder(word, count)) %>%
  ggplot(aes(x=word, y=count, fill = artDate)) +
  geom_col(show.legend = FALSE) +
  labs(x = NULL, y = NULL) +
  facet_wrap(~artDate, scales="free", ncol = 3) + 
  coord_flip()
foodpanda_plot_top5

foodpanda_1031 %>% 
  filter(artDate == as.Date("2019/11/08") | 
         artDate == as.Date("2019/12/05") | 
         artDate == as.Date("2020/01/15") |
         artDate == as.Date("2020/02/12") | 
         artDate == as.Date("2020/04/18"))  %>% 
  distinct(artUrl, .keep_all = TRUE) %>% 
  select(artDate,artTitle,artUrl)

##       artDate                                                      artTitle
## 1  2019-11-08                  [新聞]消費者被剝皮？外送平台餐食費用多高於店
## 2  2019-11-08                          [問卦]有沒有當外送可以進正妹家的卦？
## 3  2019-11-08                           [問卦]板上有人沒用過foodpanda的嗎？
## 4  2019-11-08                        Re:[問卦]板上有人沒用過foodpanda的嗎？
## 5  2019-11-08                        Re:[問卦]板上有人沒用過foodpanda的嗎？
## 6  2019-11-08                             Re:[新聞]王思聰旗下熊貓直播傳破產
## 7  2019-11-08                        Re:[問卦]板上有人沒用過foodpanda的嗎？
## 8  2019-12-05                                        [問卦]天冷熊貓還跑嗎？
## 9  2019-12-05                  [新聞]不滿外送員沒零錢面膜女爆打外送員抓下體
## 10 2019-12-05                              Re:[問卦]下雨天點外送是不是很爽?
## 11 2019-12-05 [新聞]比熊貓還稀有！蘇格蘭野貓全球剩400隻　2022年首度野放大自
## 12 2019-12-05                               [問卦]foodpanda是不是快不行了？
## 13 2019-12-05                                [問卦]好想和女熊貓做愛，怎辦？
## 14 2019-12-05                   [新聞]foodpanda刪除外送員評鑑制度　留獎勵機
## 15 2020-01-15                              [問卦]熊貓明天罷工，大家會怕嗎？
## 16 2020-01-15                                  [問卦]熊貓有必要自相殘殺嗎？
## 17 2020-01-15                               Re:[問卦]熊貓有必要自相殘殺嗎？
## 18 2020-01-15                              [問卦]可憐哪！熊貓外送師被砍獎金
## 19 2020-01-15                                      [問卦]熊貓開始罷工了嗎？
## 20 2020-01-15                          Re:[問卦]欸欸欸!聽說台灣今天熊貓罷工
## 21 2020-01-15                            [問卦]熊貓外送罷工有人被影響到嗎？
## 22 2020-01-15                                      [問卦]今天中午能定熊貓嗎
## 23 2020-01-15                                   Re:[問卦]今天中午能定熊貓嗎
## 24 2020-01-15                  [新聞]控「年前毀約」！台中60名熊貓外送員市府
## 25 2020-01-15        [新聞]熊貓今罷送最多慢1小時…外送員：萬人響應卻無人棄單
## 26 2020-01-15                     [新聞]foodpanda罷工實測！點餐後30分就拿到
## 27 2020-01-15     Re:[新聞]熊貓今罷送最多慢1小時…外送員：萬人響應卻無人棄單
## 28 2020-01-15     Re:[新聞]熊貓今罷送最多慢1小時…外送員：萬人響應卻無人棄單
## 29 2020-01-15                    [新聞]熊貓變更計薪制度內湖外送員控「變相減
## 30 2020-01-15                        [問卦]社會上為何充滿仇視外送員的八卦？
## 31 2020-02-12                    [問卦]居家自主隔離但是叫熊貓，會不會有問題
## 32 2020-02-12                  [新聞]雲林惡客狂棄訂單　20名熊貓外送員同時被
## 33 2020-02-12                  [新聞]影／「黃千千」訂的！雲林foodpanda出現2
## 34 2020-02-12                  [新聞]熊貓外送同時遭惡作劇　10餘外送員荒地「
## 35 2020-02-12                    [新聞]雲林foodpanda惡意棄單3天逾百件20外送
## 36 2020-04-18                                [問卦]為何跑熊貓會被人看不起？
## 37 2020-04-18                                    [問卦]熊貓外送年齡變高了？
## 38 2020-04-18                                 Re:[問卦]熊貓外送年齡變高了？
## 39 2020-04-18                    [問卦]有沒有叫熊貓，發現送來的是鄰居的八卦
## 40 2020-04-18                                [新聞]外送員趴趴走憂成防疫破口
## 41 2020-04-18                    [新聞]熊貓王姓外送員騎機車與汽車碰撞王男受
## 42 2020-04-18                    [新聞]驚險！熊貓外送員撞車「慘成火球」　客
##                                                      artUrl
## 1  https://www.ptt.cc/bbs/Gossiping/M.1573214540.A.679.html
## 2  https://www.ptt.cc/bbs/Gossiping/M.1573218796.A.F5B.html
## 3  https://www.ptt.cc/bbs/Gossiping/M.1573274499.A.41D.html
## 4  https://www.ptt.cc/bbs/Gossiping/M.1573278564.A.8E1.html
## 5  https://www.ptt.cc/bbs/Gossiping/M.1573278855.A.090.html
## 6  https://www.ptt.cc/bbs/Gossiping/M.1573279890.A.61F.html
## 7  https://www.ptt.cc/bbs/Gossiping/M.1573281732.A.508.html
## 8  https://www.ptt.cc/bbs/Gossiping/M.1575535036.A.FFB.html
## 9  https://www.ptt.cc/bbs/Gossiping/M.1575543109.A.D93.html
## 10 https://www.ptt.cc/bbs/Gossiping/M.1575544655.A.8C3.html
## 11 https://www.ptt.cc/bbs/Gossiping/M.1575561309.A.116.html
## 12 https://www.ptt.cc/bbs/Gossiping/M.1575562455.A.17B.html
## 13 https://www.ptt.cc/bbs/Gossiping/M.1575604895.A.1A7.html
## 14 https://www.ptt.cc/bbs/Gossiping/M.1575614882.A.A04.html
## 15 https://www.ptt.cc/bbs/Gossiping/M.1579084393.A.F0D.html
## 16 https://www.ptt.cc/bbs/Gossiping/M.1579088242.A.94B.html
## 17 https://www.ptt.cc/bbs/Gossiping/M.1579088784.A.D87.html
## 18 https://www.ptt.cc/bbs/Gossiping/M.1579106822.A.063.html
## 19 https://www.ptt.cc/bbs/Gossiping/M.1579129029.A.890.html
## 20 https://www.ptt.cc/bbs/Gossiping/M.1579133047.A.48D.html
## 21 https://www.ptt.cc/bbs/Gossiping/M.1579138472.A.14A.html
## 22 https://www.ptt.cc/bbs/Gossiping/M.1579138944.A.272.html
## 23 https://www.ptt.cc/bbs/Gossiping/M.1579139900.A.F7E.html
## 24 https://www.ptt.cc/bbs/Gossiping/M.1579151065.A.23C.html
## 25 https://www.ptt.cc/bbs/Gossiping/M.1579152088.A.C9B.html
## 26 https://www.ptt.cc/bbs/Gossiping/M.1579154317.A.E0C.html
## 27 https://www.ptt.cc/bbs/Gossiping/M.1579154864.A.DC6.html
## 28 https://www.ptt.cc/bbs/Gossiping/M.1579155632.A.F04.html
## 29 https://www.ptt.cc/bbs/Gossiping/M.1579157340.A.3DA.html
## 30 https://www.ptt.cc/bbs/Gossiping/M.1579159400.A.B4D.html
## 31 https://www.ptt.cc/bbs/Gossiping/M.1581511121.A.EBA.html
## 32 https://www.ptt.cc/bbs/Gossiping/M.1581519697.A.431.html
## 33 https://www.ptt.cc/bbs/Gossiping/M.1581524250.A.150.html
## 34 https://www.ptt.cc/bbs/Gossiping/M.1581558945.A.EC0.html
## 35 https://www.ptt.cc/bbs/Gossiping/M.1581564129.A.B7C.html
## 36 https://www.ptt.cc/bbs/Gossiping/M.1587177122.A.D43.html
## 37 https://www.ptt.cc/bbs/Gossiping/M.1587180542.A.8BE.html
## 38 https://www.ptt.cc/bbs/Gossiping/M.1587205233.A.519.html
## 39 https://www.ptt.cc/bbs/Gossiping/M.1587205611.A.A47.html
## 40 https://www.ptt.cc/bbs/Gossiping/M.1587215241.A.758.html
## 41 https://www.ptt.cc/bbs/Gossiping/M.1587225472.A.E42.html
## 42 https://www.ptt.cc/bbs/Gossiping/M.1587225632.A.ED6.html

重點新聞摘要: [武漢肺炎爆發前] 1.[問卦] foodpanda是不是快不行了？:免運取消加上合作餐廳數量減少 2.台灣熊貓罷工

[武漢肺炎爆發前] 1.居家自主隔離但是叫熊貓，會不會有問題 2.外送平台「foodpanda」近來正夯，雲林縣斗六市外送員們之間流傳，從10日開始連續三天遭惡意大量訂餐，外送員找不到訂餐者，甚至遭惡整到荒郊野外，三天已有上百件惡意棄單，粗估3天來總訂餐費用已逾7萬元，讓外送員們直呼做白工，並希望惡作劇快落幕。 3.[新聞]外送員趴趴走憂成防疫破口 4.[新聞]驚險！熊貓外送員撞車「慘成火球」

###情緒比較以LIWC字典統計每天的文章正面字的次數與負面字的次數

P <- read_file("positive.txt")
N <- read_file("negative.txt")

#將字串依,分割
#strsplit回傳list , 我們取出list中的第一個元素
P = strsplit(P, ",")[[1]]
N = strsplit(N, ",")[[1]]
# 建立dataframe 有兩個欄位word,sentiments，word欄位內容是字典向量
P = data.frame(word = P, sentiment = "positive")
N = data.frame(word = N, sentiment = "negative")

LIWC = rbind(P, N)

UberEats情緒趨勢圖

ubereats_sentiment_count = ubereats_1031 %>%
  select(artDate,word,count) %>%
  inner_join(LIWC) %>% 
  group_by(artDate,sentiment) %>%
  summarise(count=sum(count))

## Joining, by = "word"

## Warning: Column `word` joining character vector and factor, coercing into
## character vector

ubereats_sentiment_count %>%
  ggplot()+
  geom_line(aes(x=artDate,y=count,colour=sentiment))+
  scale_x_date(labels = date_format("%m/%d")) +
  geom_vline(aes(xintercept = as.numeric(artDate[which(ubereats_sentiment_count$artDate == as.Date('2019/11/25'))[1]])),colour = "red") +
  geom_vline(aes(xintercept = as.numeric(artDate[which(ubereats_sentiment_count$artDate == as.Date('2020/03/28'))[1]])),colour = "red") +
  geom_vline(aes(xintercept = as.numeric(artDate[which(ubereats_sentiment_count$artDate == as.Date('2020/04/18'))[1]])),colour = "blue") +
  geom_vline(aes(xintercept = as.numeric(artDate[which(ubereats_sentiment_count$artDate == as.Date('2019/11/14'))[1]])),colour = "blue")

ubereats_sentiment_count %>% 
  arrange(desc(count))

## # A tibble: 106 x 3
## # Groups:   artDate [63]
##    artDate    sentiment count
##    <date>     <fct>     <int>
##  1 2020-03-28 positive     16
##  2 2019-11-25 positive     15
##  3 2020-04-10 positive     14
##  4 2020-04-18 negative     13
##  5 2019-11-14 negative     11
##  6 2019-11-27 positive     11
##  7 2020-04-12 negative     11
##  8 2020-04-21 negative     11
##  9 2019-11-05 positive      9
## 10 2019-11-08 negative      9
## # ... with 96 more rows

正面情緒高峰

ubereats_1031 %>% 
  filter(artDate == as.Date("2019/11/25") |
         artDate == as.Date("2020/03/28") )  %>% 
  distinct(artUrl, .keep_all = TRUE) %>% 
  select(artDate,artTitle)

##      artDate                                    artTitle
## 1 2019-11-25        [新聞]吃得苦中苦外送員月薪打趴上班族
## 2 2020-03-28 [新聞]今年前二月機車肇事增加外送平台佔4.25%
## 3 2020-03-28  [新聞]寧夏夜市靠外送突圍下一步要走向社交電

[武漢肺炎前] 1.薪資探討

[武漢肺炎後] 1.車禍預期增加? 2.外送平台為餐飲業者轉型契機

負面情緒高峰

ubereats_1031 %>% 
  filter(artDate == as.Date("2019/11/14") |
         artDate == as.Date("2020/04/18") ) %>% 
  distinct(artUrl, .keep_all = TRUE) %>% 
  select(artDate,artTitle)

##      artDate                                   artTitle
## 1 2019-11-14    Re:[問卦]UberEats有限制一定要用機車嗎？
## 2 2019-11-14 [新聞]UBEREATS送餐員撞傷女騎士業務過失傷害
## 3 2020-04-18                 [問卦]ubereats被盜刷四千元
## 4 2020-04-18             [新聞]外送員趴趴走憂成防疫破口

[武漢肺炎前] 1.薪資探討

[武漢肺炎後] 1.車禍預期增加? 2.外送平台為餐飲業者轉型契機

ubereats與疫情相關的文章

covid <- c("防疫","疫情","檢疫","居家","檢疫","武漢","肺炎","武漢肺炎","COVID-19","covid-19","COVID19","covid19")
data_ubereats_co <- ubereats_data %>% 
  filter(ubereats_data$word %in% covid) %>% 
  distinct(artUrl, .keep_all = TRUE) %>% 
  select(artTitle,artDate,word,count)
data_ubereats_co

##                                       artTitle    artDate word count
## 1   [問卦]外送人員是不是武漢肺炎的高風險職業？ 2020-01-28 居家     1
## 2          [問卦]這波疫情會讓外送平台發大財嗎? 2020-02-04 疫情     1
## 3    [問卦]Ubereat外送員說他有送過隔離的人怎辦 2020-02-09 居家     1
## 4              [問卦]台南ubereat現在還好跑嗎？ 2020-02-13 武漢     1
## 5          Re:[問卦]外送員會不會變成防疫破口？ 2020-03-25 居家     5
## 6  [新聞]今年前二月機車肇事增加外送平台佔4.25% 2020-03-28 武漢     1
## 7   [新聞]寧夏夜市靠外送突圍下一步要走向社交電 2020-03-28 疫情     7
## 8    [新聞]5業者組外送國家隊救餐飲！UberEats、 2020-04-10 疫情     3
## 9   [新聞]外送員慘了！Gogoro嚴查違規車強制升級 2020-04-15 肺炎     1
## 10      [新聞]點餐平台不提供口罩外送員自抗風險 2020-04-17 防疫     4
## 11              [新聞]外送員趴趴走憂成防疫破口 2020-04-18 居家     4
## 12  [新聞]UberEats的復仇！早餐店嗆：嘴巴閉閉乖 2020-04-21 疫情     1

討論議題重點擷取 [相關議題探討] 1.外送員為高風險族群、點餐平台不提供口罩外送員自抗風險 2.這波疫情會讓外送平台發大財嗎? 3.車禍議題 4.新冠疫情延燒，外送平台生意增加，卻傳出有連鎖早餐店不滿歧視外送員，在取餐備註上寫著「嘴巴閉閉乖乖旁邊等」，引起外送員不滿，紛紛前往粉絲專頁洗版抗議。總公司得知後火速開鍘，馬上勒令該分店停權並要求品牌商譽賠償。

[產業結盟] 1.5業者組外送國家隊救餐飲！ 2.寧夏夜市靠外送突圍

Foodpanda情緒趨勢圖

foodpanda_sentiment_count = foodpanda_1031 %>%
  select(artDate,word,count) %>%
  inner_join(LIWC) %>% 
  group_by(artDate,sentiment) %>%
  summarise(count=sum(count))

## Joining, by = "word"

## Warning: Column `word` joining character vector and factor, coercing into
## character vector

foodpanda_sentiment_count %>%
  ggplot()+
  geom_line(aes(x=artDate,y=count,colour=sentiment))+
  scale_x_date(labels = date_format("%m/%d")) +
  geom_vline(aes(xintercept = as.numeric(artDate[which(foodpanda_sentiment_count$artDate == as.Date('2020/02/12'))[1]])),colour = "blue") +
  geom_vline(aes(xintercept = as.numeric(artDate[which(foodpanda_sentiment_count$artDate == as.Date('2020/01/15'))[1]])),colour = "blue") +
  geom_vline(aes(xintercept = as.numeric(artDate[which(foodpanda_sentiment_count$artDate == as.Date('2020/04/10'))[1]])),colour = "red") +
  geom_vline(aes(xintercept = as.numeric(artDate[which(foodpanda_sentiment_count$artDate == as.Date('2019/11/09'))[1]])),colour = "blue")

foodpanda_sentiment_count %>% 
  arrange(desc(count))

## # A tibble: 205 x 3
## # Groups:   artDate [117]
##    artDate    sentiment count
##    <date>     <fct>     <int>
##  1 2020-02-12 negative     75
##  2 2020-01-15 negative     50
##  3 2020-04-10 positive     34
##  4 2019-11-09 negative     30
##  5 2019-12-13 negative     29
##  6 2020-01-15 positive     29
##  7 2019-11-29 positive     27
##  8 2020-01-13 positive     27
##  9 2020-01-13 negative     26
## 10 2019-11-09 positive     25
## # ... with 195 more rows

正面情緒高峰

foodpanda_1031 %>% 
  filter(artDate == as.Date("2020/04/10"))  %>% 
  distinct(artUrl, .keep_all = TRUE) %>% 
  select(artDate,artTitle)

##      artDate                                    artTitle
## 1 2020-04-10 [新聞]安心配送再升級！foodpanda發萬瓶酒精給
## 2 2020-04-10   [新聞]5業者組外送國家隊救餐飲！UberEats、
## 3 2020-04-10              [問卦]外送員也適用紓困方案嗎？

[武漢肺炎後] 1.外送平台公司的防疫措施 2.政府政策 3.外送員也適用紓困方案嗎？

負面情緒高峰

foodpanda_1031 %>% 
  filter(artDate == as.Date("2020/02/12") |
         artDate == as.Date("2020/01/15") |
         artDate == as.Date("2019/11/09") ) %>% 
  distinct(artUrl, .keep_all = TRUE) %>% 
  select(artDate,artTitle)

##       artDate                                                  artTitle
## 1  2019-11-09                         Re:[新聞]王思聰旗下熊貓直播傳破產
## 2  2019-11-09                    Re:[問卦]板上有人沒用過foodpanda的嗎？
## 3  2019-11-09                [新聞]foodpanda下班逛東區百貨…樓管「禁現樓
## 4  2019-11-09                    [問卦]熊貓是不是解決了生活機能問題啊？
## 5  2019-11-09                 Re:[問卦]熊貓是不是解決了生活機能問題啊？
## 6  2019-11-09                               [問卦]有沒有foodpanda的卦？
## 7  2020-01-15                          [問卦]熊貓明天罷工，大家會怕嗎？
## 8  2020-01-15                              [問卦]熊貓有必要自相殘殺嗎？
## 9  2020-01-15                           Re:[問卦]熊貓有必要自相殘殺嗎？
## 10 2020-01-15                          [問卦]可憐哪！熊貓外送師被砍獎金
## 11 2020-01-15                                  [問卦]熊貓開始罷工了嗎？
## 12 2020-01-15                      Re:[問卦]欸欸欸!聽說台灣今天熊貓罷工
## 13 2020-01-15                        [問卦]熊貓外送罷工有人被影響到嗎？
## 14 2020-01-15                                  [問卦]今天中午能定熊貓嗎
## 15 2020-01-15                               Re:[問卦]今天中午能定熊貓嗎
## 16 2020-01-15              [新聞]控「年前毀約」！台中60名熊貓外送員市府
## 17 2020-01-15    [新聞]熊貓今罷送最多慢1小時…外送員：萬人響應卻無人棄單
## 18 2020-01-15                 [新聞]foodpanda罷工實測！點餐後30分就拿到
## 19 2020-01-15 Re:[新聞]熊貓今罷送最多慢1小時…外送員：萬人響應卻無人棄單
## 20 2020-01-15 Re:[新聞]熊貓今罷送最多慢1小時…外送員：萬人響應卻無人棄單
## 21 2020-01-15                [新聞]熊貓變更計薪制度內湖外送員控「變相減
## 22 2020-01-15                    [問卦]社會上為何充滿仇視外送員的八卦？
## 23 2020-02-12                [問卦]居家自主隔離但是叫熊貓，會不會有問題
## 24 2020-02-12              [新聞]雲林惡客狂棄訂單　20名熊貓外送員同時被
## 25 2020-02-12              [新聞]影／「黃千千」訂的！雲林foodpanda出現2
## 26 2020-02-12              [新聞]熊貓外送同時遭惡作劇　10餘外送員荒地「
## 27 2020-02-12                [新聞]雲林foodpanda惡意棄單3天逾百件20外送

[武漢肺炎前] 1.罷工議題

[武漢肺炎後] 1.居家自主隔離但是叫熊貓 2.雲林集體棄單

foodpanda與疫情相關的文章

covid <- c("防疫","疫情","檢疫","居家","檢疫","武漢","肺炎","武漢肺炎","COVID-19","covid-19","COVID19","covid19")
data_foodpanda_co <- foodpanda_data %>% 
  filter(foodpanda_data$word %in% covid) %>% 
  distinct(artUrl, .keep_all = TRUE) %>% 
  select(artTitle,artDate,word,count)
data_foodpanda_co

##                                                         artTitle    artDate
## 1                                           [問卦]熊貓仔要正名!! 2019-10-16
## 2                     [問卦]外送人員是不是武漢肺炎的高風險職業？ 2020-01-28
## 3                             [問卦]還沒提供外送的店家在想什麼？ 2020-01-30
## 4                            [問卦]這波疫情會讓外送平台發大財嗎? 2020-02-04
## 5                               [問卦]吳柏毅怎不乘勝追擊打趴熊貓 2020-02-11
## 6                     [問卦]居家自主隔離但是叫熊貓，會不會有問題 2020-02-12
## 7                           [問卦]熊貓、吳博弈外送怎麼都不見了?? 2020-02-28
## 8                    [新聞]外送員不爽上樓！朝女兒「咳嗽噴口水」… 2020-03-01
## 9                     [新聞]熊貓免運加麥當勞超殺優惠網友狂買「25 2020-03-08
## 10                                    [問卦]居家隔離該選哪間外送 2020-03-20
## 11 [新聞]「爆兇」嫩妹居家檢疫落跑買飯　蘆洲警問怎不叫外送…她奶聲 2020-03-23
## 12                   [新聞]foodpanda啟動「全台店家紓困轉型專案」 2020-03-27
## 13                    [新聞]寧夏夜市靠外送突圍下一步要走向社交電 2020-03-28
## 14                    [新聞]免費幫投保！熊貓外送員「染疫可領1萬5 2020-03-31
## 15                  [新聞]咳嗽女現金放門外付款！外送員不安「在隔 2020-04-06
## 16                  [新聞]沒遊客才能放鬆！香港熊貓睽違九年首自然 2020-04-09
## 17                   [新聞]安心配送再升級！foodpanda發萬瓶酒精給 2020-04-10
## 18                     [新聞]5業者組外送國家隊救餐飲！UberEats、 2020-04-10
## 19                                [問卦]外送員也適用紓困方案嗎？ 2020-04-10
## 20                            [問卦]熊貓的處置需不需要超前部屬？ 2020-04-15
## 21                     Re:[問卦]騎gogoro吃到飽方案外送被罰死怎辦 2020-04-16
## 22                        [新聞]點餐平台不提供口罩外送員自抗風險 2020-04-17
## 23                                [新聞]外送員趴趴走憂成防疫破口 2020-04-18
##    word count
## 1  居家     1
## 2  居家     1
## 3  武漢     1
## 4  疫情     1
## 5  肺炎     1
## 6  居家     1
## 7  武漢     1
## 8  武漢     1
## 9  疫情     2
## 10 居家     1
## 11 檢疫     9
## 12 肺炎     1
## 13 疫情     7
## 14 防疫     6
## 15 居家     3
## 16 疫情     1
## 17 防疫     1
## 18 疫情     3
## 19 疫情     1
## 20 疫情     1
## 21 疫情     1
## 22 防疫     4
## 23 居家     4

討論議題重點擷取 [相關議題探討] 1.外送員為高風險族群、點餐平台不提供口罩外送員自抗風險 2.這波疫情會讓外送平台發大財嗎? 3.免費幫投保！熊貓外送員「染疫可領1萬5補償金」 2個月內有上線 4.安心配送再升級！foodpanda發萬瓶酒精

[產業結盟] 1.foodpanda啟動全台店家紓困轉型專案 2.業者組外送國家隊救餐飲！ 3.外送員也適用紓困方案嗎？

###兩大平台交叉比較聲量趨勢:在PTT的媒體上，Foodpanda的聲量普遍高於UberEats，能是因為Foodpanda的使用者較多，且近期有較多被討論的議題(重大車禍、勞雇關係和罷工等)，這也影響了情緒的表現上Foodpanda的起伏會有較大的高低落差。

foodpanda_1031_day<- foodpanda_1031_day%>% mutate(Brand="panda")
ubereats_1031_day<- ubereats_1031_day%>% mutate(Brand="ubereats")
volume<-bind_rows(foodpanda_1031_day,ubereats_1031_day)%>%
  ggplot(aes(x = artDate, y = count)) +
  geom_line(aes(color = Brand), size = 0.5)+ 
  scale_x_date(labels = date_format("%m/%d"))+ 
  ggtitle("品牌聲量比較") 
volume

正情緒趨勢

foodpanda_sentiment_count_p<-foodpanda_sentiment_count %>% filter(sentiment=="positive") %>% mutate(Brand="panda")

ubereats_sentiment_count_p<-ubereats_sentiment_count %>% filter(sentiment=="positive") %>% mutate(Brand="ubereats")

sentiment_p<-bind_rows(foodpanda_sentiment_count_p,ubereats_sentiment_count_p)%>%
  ggplot(aes(x = artDate, y = count)) +
  geom_line(aes(color = Brand), size = 0.5)+ 
  scale_x_date(labels = date_format("%m/%d"))+
  geom_vline(aes(xintercept = as.numeric(artDate[which(foodpanda_sentiment_count_p$artDate == as.Date('2020/04/10'))[1]])),colour = "red")+
  geom_vline(aes(xintercept = as.numeric(artDate[which(foodpanda_sentiment_count_p$artDate == as.Date('2020/03/31'))[1]])),colour = "red")+ 
  geom_vline(aes(xintercept = as.numeric(artDate[which(foodpanda_sentiment_count_p$artDate == as.Date('2020/03/28'))[1]])),colour = "blue") +
  ggtitle("正情緒趨勢比較") 
sentiment_p

1.’2020/03/31’:免費幫投保 2.’2020/04/10’:foodpanda發萬瓶酒精 [ubereats]: 1.’2020/03/28’:寧夏夜市靠外送突圍

負情緒趨勢

foodpanda_sentiment_count_n<-foodpanda_sentiment_count %>% filter(sentiment=="negative") %>% mutate(Brand="panda")

ubereats_sentiment_count_n<-ubereats_sentiment_count %>% filter(sentiment=="negative") %>% mutate(Brand="ubereats")

sentiment_n<-bind_rows(foodpanda_sentiment_count_n,ubereats_sentiment_count_n)%>%
  ggplot(aes(x = artDate, y = count)) +
  geom_line(aes(color = Brand), size = 0.5)+ 
  scale_x_date(labels = date_format("%m/%d"))+
  geom_vline(aes(xintercept = as.numeric(artDate[which(foodpanda_sentiment_count_n$artDate == as.Date('2020/02/12'))[1]])),colour = "blue") +
  geom_vline(aes(xintercept = as.numeric(artDate[which(foodpanda_sentiment_count_n$artDate == as.Date('2020/01/15'))[1]])),colour = "blue")+ 
  ggtitle("負正情緒趨勢比較") 
sentiment_n

foodpanda 1.’2020/01/15’:熊貓罷工 2.’2020/02/12’:雲林foodpanda惡意棄單

結論

1.武漢肺炎爆發後雖然外送平台的使用率上升，但在ptt的討論上聲量並沒有立即增加 2.討論議題逐漸浮出(紓困方案、產業合作和交通安全等) 3.通常與政府議題相關報導會反映出較為正向的情緒 4.與疫情相關的文章，通常不會有較大的情緒表現，若有通常偏正向情緒

Uber eat 停權 ptt

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系統參數設定

UberEats聲量趨勢圖

Foodpanda聲量趨勢圖

UberEats情緒趨勢圖

Foodpanda情緒趨勢圖

結論

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系統參數設定

UberEats聲量趨勢圖

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UberEats情緒趨勢圖

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結論

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合法的

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Uber eat 停權 ptt