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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': ## ## filter, lag## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, unionlibrary(stringr) library(tidytext) library(wordcloud2) library(data.table)## ## Attaching package: 'data.table'## The following objects are masked from 'package:dplyr': ## ## between, first, lastlibrary(ggplot2) library(reshape2)## ## Attaching package: 'reshape2'## The following objects are masked from 'package:data.table': ## ## dcast, meltlibrary(wordcloud)## Loading required package: RColorBrewerlibrary(tidyr)## ## Attaching package: 'tidyr'## The following object is masked from 'package:reshape2': ## ## smithslibrary(readr) library(scales)## ## Attaching package: 'scales'## The following object is masked from 'package:readr': ## ## col_factorrequire(jiebaR)## Loading required package: jiebaR## Loading required package: jiebaRDrequire(widyr)## Loading required package: widyrrequire(NLP)## Loading required package: NLP## ## Attaching package: 'NLP'## The following object is masked from 'package:ggplot2': ## ## annotaterequire(ggraph)## Loading required package: ggraphrequire(igraph)## Loading required package: igraph## ## Attaching package: 'igraph'## The following object is masked from 'package:tidyr': ## ## crossing## The following objects are masked from 'package:dplyr': ## ## as_data_frame, groups, union## The following objects are masked from 'package:stats': ## ## decompose, spectrum## The following object is masked from 'package:base': ## ## union

###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## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family not ## found in Windows font database ## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family not ## found in Windows font database ## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family not ## found in Windows font database## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font ## family not found in Windows font database ## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font ## family not found in Windows font database ## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font ## family not found in Windows font database ## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font ## family not found in Windows font database ## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font ## family not found in Windows font database ## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font ## family not found in Windows font database ## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font ## family not found in Windows font database ## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font ## family not found in Windows font database ## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font ## family not found in Windows font database ## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font ## family not found in Windows font database## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, : ## font family not found in Windows font database## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font ## family not found in Windows font database

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 239ubereats_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 //www.ptt.cc/bbs/Gossiping/M.1573204052.A.82C.html ## 2 //www.ptt.cc/bbs/Gossiping/M.1573214540.A.679.html ## 3 //www.ptt.cc/bbs/Gossiping/M.1574428391.A.B79.html ## 4 //www.ptt.cc/bbs/Gossiping/M.1574445517.A.94B.html ## 5 //www.ptt.cc/bbs/Gossiping/M.1585394042.A.C82.html ## 6 //www.ptt.cc/bbs/Gossiping/M.1585410190.A.3CD.html ## 7 //www.ptt.cc/bbs/Gossiping/M.1586514906.A.602.html ## 8 //www.ptt.cc/bbs/Gossiping/M.1587171437.A.983.html ## 9 //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$artDate > as.Date("2019/10/31"))foodpanda_1031_day <- foodpanda_1031 %>% group_by(artDate) %>% summarise(count = n()) %>% arrange(desc(count)) foodpanda_1031_day## # A tibble: 136 x 2 ## artDate count ## <date> <int> ## 1 2020-01-15 859 ## 2 2020-02-12 840 ## 3 2020-01-16 706 ## 4 2020-01-13 678 ## 5 2020-01-18 668 ## 6 2019-11-09 618 ## 7 2019-12-05 552 ## 8 2019-11-08 538 ## 9 2020-04-18 470 ## 10 2020-02-14 455 ## # ... with 126 more rowsfoodpanda_day_1031_plot <- foodpanda_1031_day %>% ggplot(aes(x = artDate, y = count)) + geom_line(color = "purple", size = 1) + scale_x_date(labels = date_format("%Y/%m/%d")) + geom_vline(xintercept = as.numeric(as.Date("2020-01-21"),col='red',size = 1))+ ggtitle("Foodpanda 10/31後每日討論篇數") + xlab("日期") + ylab("數量") + theme(text = element_text(family = "Heiti TC Light")) foodpanda_day_1031_plot## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font ## family not found in Windows font database ## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font ## family not found in Windows font database ## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font ## family not found in Windows font database ## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font ## family not found in Windows font database ## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font ## family not found in Windows font database ## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font ## family not found in Windows font database ## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font ## family not found in Windows font database ## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font ## family not found in Windows font database ## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font ## family not found in Windows font database ## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font ## family not found in Windows font database## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, : ## font family not found in Windows font database## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font ## family not found in Windows font database

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 455foodpanda_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 //www.ptt.cc/bbs/Gossiping/M.1573214540.A.679.html ## 2 //www.ptt.cc/bbs/Gossiping/M.1573218796.A.F5B.html ## 3 //www.ptt.cc/bbs/Gossiping/M.1573274499.A.41D.html ## 4 //www.ptt.cc/bbs/Gossiping/M.1573278564.A.8E1.html ## 5 //www.ptt.cc/bbs/Gossiping/M.1573278855.A.090.html ## 6 //www.ptt.cc/bbs/Gossiping/M.1573279890.A.61F.html ## 7 //www.ptt.cc/bbs/Gossiping/M.1573281732.A.508.html ## 8 //www.ptt.cc/bbs/Gossiping/M.1575535036.A.FFB.html ## 9 //www.ptt.cc/bbs/Gossiping/M.1575543109.A.D93.html ## 10 //www.ptt.cc/bbs/Gossiping/M.1575544655.A.8C3.html ## 11 //www.ptt.cc/bbs/Gossiping/M.1575561309.A.116.html ## 12 //www.ptt.cc/bbs/Gossiping/M.1575562455.A.17B.html ## 13 //www.ptt.cc/bbs/Gossiping/M.1575604895.A.1A7.html ## 14 //www.ptt.cc/bbs/Gossiping/M.1575614882.A.A04.html ## 15 //www.ptt.cc/bbs/Gossiping/M.1579084393.A.F0D.html ## 16 //www.ptt.cc/bbs/Gossiping/M.1579088242.A.94B.html ## 17 //www.ptt.cc/bbs/Gossiping/M.1579088784.A.D87.html ## 18 //www.ptt.cc/bbs/Gossiping/M.1579106822.A.063.html ## 19 //www.ptt.cc/bbs/Gossiping/M.1579129029.A.890.html ## 20 //www.ptt.cc/bbs/Gossiping/M.1579133047.A.48D.html ## 21 //www.ptt.cc/bbs/Gossiping/M.1579138472.A.14A.html ## 22 //www.ptt.cc/bbs/Gossiping/M.1579138944.A.272.html ## 23 //www.ptt.cc/bbs/Gossiping/M.1579139900.A.F7E.html ## 24 //www.ptt.cc/bbs/Gossiping/M.1579151065.A.23C.html ## 25 //www.ptt.cc/bbs/Gossiping/M.1579152088.A.C9B.html ## 26 //www.ptt.cc/bbs/Gossiping/M.1579154317.A.E0C.html ## 27 //www.ptt.cc/bbs/Gossiping/M.1579154864.A.DC6.html ## 28 //www.ptt.cc/bbs/Gossiping/M.1579155632.A.F04.html ## 29 //www.ptt.cc/bbs/Gossiping/M.1579157340.A.3DA.html ## 30 //www.ptt.cc/bbs/Gossiping/M.1579159400.A.B4D.html ## 31 //www.ptt.cc/bbs/Gossiping/M.1581511121.A.EBA.html ## 32 //www.ptt.cc/bbs/Gossiping/M.1581519697.A.431.html ## 33 //www.ptt.cc/bbs/Gossiping/M.1581524250.A.150.html ## 34 //www.ptt.cc/bbs/Gossiping/M.1581558945.A.EC0.html ## 35 //www.ptt.cc/bbs/Gossiping/M.1581564129.A.B7C.html ## 36 //www.ptt.cc/bbs/Gossiping/M.1587177122.A.D43.html ## 37 //www.ptt.cc/bbs/Gossiping/M.1587180542.A.8BE.html ## 38 //www.ptt.cc/bbs/Gossiping/M.1587205233.A.519.html ## 39 //www.ptt.cc/bbs/Gossiping/M.1587205611.A.A47.html ## 40 //www.ptt.cc/bbs/Gossiping/M.1587215241.A.758.html ## 41 //www.ptt.cc/bbs/Gossiping/M.1587225472.A.E42.html ## 42 //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 vectorubereats_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 vectorfoodpanda_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