ch0.套件取得及資料載入

套件

library(data.table)

## Warning: package 'data.table' was built under R version 4.0.4

library(ggplot2)

## Warning: package 'ggplot2' was built under R version 4.0.4

library(dplyr)

## Warning: package 'dplyr' was built under R version 4.0.4

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:data.table':
## 
##     between, first, last

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(jiebaR)

## Warning: package 'jiebaR' was built under R version 4.0.4

## Loading required package: jiebaRD

## Warning: package 'jiebaRD' was built under R version 4.0.4

library(tidytext)

## Warning: package 'tidytext' was built under R version 4.0.4

library(stringr)
library(tm)

## Warning: package 'tm' was built under R version 4.0.5

## Loading required package: NLP

## 
## Attaching package: 'NLP'

## The following object is masked from 'package:ggplot2':
## 
##     annotate

library(servr)

## Warning: package 'servr' was built under R version 4.0.5

library(topicmodels)

## Warning: package 'topicmodels' was built under R version 4.0.5

library(purrr)

## Warning: package 'purrr' was built under R version 4.0.4

## 
## Attaching package: 'purrr'

## The following object is masked from 'package:data.table':
## 
##     transpose

require(RColorBrewer)

## Loading required package: RColorBrewer

require(tidyr)

## Loading required package: tidyr

## Warning: package 'tidyr' was built under R version 4.0.4

require(servr)
mycolors <- colorRampPalette(brewer.pal(8, "Set3"))(20)

資料描述

Ptt : 八卦版、武漢肺炎版時間 : 2020/1/1-2021/5/15 關鍵字 : 陳時中目的 : 與陳時中一同出現的資料分類如何，有哪些資料

metadata <- fread("tttttt_articleMetaData.csv", encoding = "UTF-8")

metadata %>% 
  mutate(artDate = as.Date(artDate)) %>%
  group_by(artDate) %>%
  summarise(count = n())%>%
  ggplot(aes(artDate,count))+
    geom_line(color="red")+
    geom_point()

> 關於陳時中的討論在2020-3-26達最高峰，另一起高峰在2020-8-19~2020-8-31，最低是在2020年7月，是疫情較不嚴重的區段。

#Ch1. Document Term Matrix (DTM)

資料前處理

移除PTT貼新聞時會出現的格式用字

metadata <- metadata %>% 
  mutate(sentence=gsub("媒體來源|記者署名|完整新聞標題|完整新聞內文|完整新聞連結|(或短網址)|備註|備註請放最後面|違者新聞文章刪除|張貼問卦請注意|充實文章內容|是否有專板|本板並非萬能問板|一天只能張貼|自刪及被刪也算兩篇之內|超貼者將被水桶|本看板嚴格禁止政治問卦|發文問卦前請先仔細閱讀相關板規|未滿30繁體中文字水桶3個月|嚴重者以鬧板論", "", sentence))

bigram

jieba_tokenizer = worker()

# unnest_tokens 使用的bigram分詞函數
# Input: a character vector
# Output: a list of character vectors of the same length
jieba_bigram <- function(t) {
  lapply(t, function(x) {
    if(nchar(x)>1){
      tokens <- segment(x, jieba_tokenizer)
      bigram<- ngrams(tokens, 2)
      bigram <- lapply(bigram, paste, collapse = " ")
      unlist(bigram)
    }
  })
}

metadata_bigram <- metadata %>%
  unnest_tokens(bigram, sentence, token = jieba_bigram)

metadata_bigram %>%
  filter(!str_detect(bigram, regex("[0-9a-zA-Z]"))) %>%
  count(bigram, sort = TRUE)

trigram

jieba_trigram <- function(t) {
  lapply(t, function(x) {
    if(nchar(x)>1){
      tokens <- segment(x, jieba_tokenizer)
      ngram<- ngrams(unlist(tokens), 3)
      ngram <- lapply(ngram, paste, collapse = " ")
      unlist(ngram)
    }
  })
}

metadata_trigram <- metadata %>%
  unnest_tokens(ngrams, sentence, token = jieba_trigram)
metadata_trigram %>%
  filter(!str_detect(ngrams, regex("[0-9a-zA-Z]"))) %>%
  count(ngrams, sort = TRUE)

Remove stop words in bigram

# load stop words
stop_words <- scan(file = "./stop_words.txt", what=character(),sep='\n', 
                   encoding='utf-8',fileEncoding='utf-8')

## Warning in scan(file = "./stop_words.txt", what = character(), sep = "\n", : 輸
## 入連結 './stop_words.txt' 中的輸入不正確

metadata_bigram %>%
  filter(!str_detect(bigram, regex("[0-9a-zA-Z]"))) %>%
  separate(bigram, c("word1", "word2"), sep = " ") %>% 
  filter(!(word1 %in% stop_words), !(word2 %in% stop_words)) %>%
  count(word1, word2, sort = TRUE) %>%
  unite_("bigram", c("word1","word2"), sep=" ")

Remove the stopwords in trigram

metadata_trigram %>%
  filter(!str_detect(ngrams, regex("[0-9a-zA-Z]"))) %>%
  separate(ngrams, c("word1", "word2", "word3"), sep = " ") %>% 
  filter(!(word1 %in% stop_words), !(word2 %in% stop_words), !(word3 %in% stop_words)) %>%
  count(word1, word2, word3, sort = TRUE) %>%
  unite_("ngrams", c("word1", "word2", "word3"), sep=" ")

使用自建字典及停用字字典

jieba_tokenizer = worker(user="word.txt",stop_word="stop_words.txt")
news_tokenizer <- function(t) {
  lapply(t, function(x) {
    if(nchar(x)>1){
      tokens <- segment(x, jieba_tokenizer)
      # 去掉字串長度爲1的詞彙
      tokens <- tokens[nchar(tokens)>1]
      return(tokens)
    }
  })
}

計算每篇文章各token出現次數

tokens <- metadata %>%
  unnest_tokens(word, sentence, token=news_tokenizer) %>%
  filter((!str_detect(word, regex("[0-9a-zA-Z]")))) %>%
  count(artUrl, word) %>%
  rename(count=n)
tokens %>% head(20)

將資料轉換為Document Term Matrix (DTM)

dtm <-tokens %>% cast_dtm(artUrl, word, count)
dtm

## <<DocumentTermMatrix (documents: 3546, terms: 41345)>>
## Non-/sparse entries: 340694/146268676
## Sparsity           : 100%
## Maximal term length: 10
## Weighting          : term frequency (tf)

inspect(dtm[1:10,1:10])

## <<DocumentTermMatrix (documents: 10, terms: 10)>>
## Non-/sparse entries: 21/79
## Sparsity           : 79%
## Maximal term length: 2
## Weighting          : term frequency (tf)
## Sample             :
##                                                           Terms
## Docs                                                       一出 人口 人物 上午
##   https://www.ptt.cc/bbs/Gossiping/M.1580201716.A.D4A.html    1    1    2    1
##   https://www.ptt.cc/bbs/Gossiping/M.1580218136.A.251.html    0    0    2    0
##   https://www.ptt.cc/bbs/Gossiping/M.1580228124.A.03A.html    0    0    0    0
##   https://www.ptt.cc/bbs/Gossiping/M.1580473375.A.390.html    0    0    0    0
##   https://www.ptt.cc/bbs/Gossiping/M.1580473872.A.066.html    0    0    0    0
##   https://www.ptt.cc/bbs/Gossiping/M.1580621894.A.4DE.html    0    0    0    0
##   https://www.ptt.cc/bbs/Gossiping/M.1580632630.A.857.html    0    0    0    0
##   https://www.ptt.cc/bbs/Gossiping/M.1580637593.A.E42.html    0    0    0    0
##   https://www.ptt.cc/bbs/Gossiping/M.1580638519.A.26D.html    0    0    0    0
##   https://www.ptt.cc/bbs/Gossiping/M.1580641092.A.CD8.html    0    0    0    0
##                                                           Terms
## Docs                                                       口罩 工作 不當 中央
##   https://www.ptt.cc/bbs/Gossiping/M.1580201716.A.D4A.html    6    1    2    1
##   https://www.ptt.cc/bbs/Gossiping/M.1580218136.A.251.html    1    0    1    0
##   https://www.ptt.cc/bbs/Gossiping/M.1580228124.A.03A.html    4    1    0    0
##   https://www.ptt.cc/bbs/Gossiping/M.1580473375.A.390.html    1    0    0    0
##   https://www.ptt.cc/bbs/Gossiping/M.1580473872.A.066.html    0    0    0    3
##   https://www.ptt.cc/bbs/Gossiping/M.1580621894.A.4DE.html   14    1    0    3
##   https://www.ptt.cc/bbs/Gossiping/M.1580632630.A.857.html    0    0    0    0
##   https://www.ptt.cc/bbs/Gossiping/M.1580637593.A.E42.html    0    0    0    0
##   https://www.ptt.cc/bbs/Gossiping/M.1580638519.A.26D.html    0    0    0    1
##   https://www.ptt.cc/bbs/Gossiping/M.1580641092.A.CD8.html    0    0    0    0
##                                                           Terms
## Docs                                                       介入 內部
##   https://www.ptt.cc/bbs/Gossiping/M.1580201716.A.D4A.html    2    1
##   https://www.ptt.cc/bbs/Gossiping/M.1580218136.A.251.html    0    0
##   https://www.ptt.cc/bbs/Gossiping/M.1580228124.A.03A.html    0    0
##   https://www.ptt.cc/bbs/Gossiping/M.1580473375.A.390.html    0    0
##   https://www.ptt.cc/bbs/Gossiping/M.1580473872.A.066.html    0    0
##   https://www.ptt.cc/bbs/Gossiping/M.1580621894.A.4DE.html    0    0
##   https://www.ptt.cc/bbs/Gossiping/M.1580632630.A.857.html    0    0
##   https://www.ptt.cc/bbs/Gossiping/M.1580637593.A.E42.html    0    0
##   https://www.ptt.cc/bbs/Gossiping/M.1580638519.A.26D.html    0    0
##   https://www.ptt.cc/bbs/Gossiping/M.1580641092.A.CD8.html    0    0

ch2. 主題模型

建立LDA模型

lda <- LDA(dtm, k = 2, control = list(seed = 2021,alpha = 2,delta=0.1),method = "Gibbs") #調整alpha即delta
lda

## A LDA_Gibbs topic model with 2 topics.

利用LDA模型建立phi矩陣

topics_words <- tidy(lda, matrix = "beta") # 注意，在tidy function裡面要使用"beta"來取出Phi矩陣。
colnames(topics_words) <- c("topic", "term", "phi")
topics_words

尋找Topic的代表字

terms依照各主題的phi值由大到小排序，列出前10大

topics_words %>%
  group_by(topic) %>%
  top_n(10, phi) %>%
  ungroup() %>%
  mutate(top_words = reorder_within(term,phi,topic)) %>%
  ggplot(aes(x = top_words, y = phi, fill = as.factor(topic))) +
  geom_col(show.legend = FALSE) +
  facet_wrap(~ topic, scales = "free") +
  coord_flip() +
  scale_x_reordered()

Topic 1 可能與武漢疫情相關，Topic 2 可能與開放美豬相關

#ch3. 尋找最佳主題數

建立更多主題的主題模型

嘗試2、3、4、5個主題數，將結果存起來，再做進一步分析。此部分需要跑一段時間，已經將跑完的檔案存成ldas_result.rdata，可以直接載入

# ldas = c()
# topics = c(2,3,4,5)
# for(topic in topics){
#   start_time <- Sys.time()
#   lda <- LDA(dtm, k = topic, control = list(seed = 2021,alpha = 2,delta=0.1),method = "Gibbs")
#   ldas =c(ldas,lda)
#   print(paste(topic ,paste("topic(s) and use time is ", Sys.time() -start_time)))
#   save(ldas,file = "ldas_result.rdata") # 將模型輸出成檔案
# }

載入每個主題的LDA結果

load("ldas_result.rdata")

ch4. LDA分析

選定6個主題數的主題模型

the_lda = ldas[[3]] ## 選定topic 為 4 的結果

topics_words <- tidy(the_lda, matrix = "beta") # 注意，在tidy function裡面要使用"beta"來取出Phi矩陣。
colnames(topics_words) <- c("topic", "term", "phi")
topics_words %>% arrange(desc(phi)) %>% head(10)

terms依照各主題的phi值由大到小排序

topics_words %>%
  group_by(topic) %>%
  top_n(15, phi) %>%
  ungroup() %>%
  ggplot(aes(x = reorder_within(term,phi,topic), y = phi, fill = as.factor(topic))) +
  geom_col(show.legend = FALSE) +
  facet_wrap(~ topic, scales = "free") +
  coord_flip() +
  scale_x_reordered()

去除共通詞彙，

removed_word = c("陳時中","目前","部長","市長","疫情","台北")

topics_words %>%
  filter(!term  %in% removed_word) %>%
  group_by(topic) %>%
  top_n(15, phi) %>%
  ungroup() %>%
  ggplot(aes(x = reorder_within(term,phi,topic), y = phi, fill = as.factor(topic))) +
  geom_col(show.legend = FALSE) +
  facet_wrap(~ topic, scales = "free") +
  coord_flip() +
  scale_x_reordered()

主題命名

topics_name = c("檢疫狀況","陳時中個人新聞","疫苗接種","美豬事件")

Document 主題分佈

# for every document we have a probability distribution of its contained topics
tmResult <- posterior(the_lda)
doc_pro <- tmResult$topics
document_topics <- doc_pro[metadata$artUrl,]
document_topics_df =data.frame(document_topics)
colnames(document_topics_df) = topics_name
rownames(document_topics_df) = NULL
news_topic = cbind(metadata,document_topics_df)

現在我們看每一篇的文章分佈了！

查看特定主題的文章

透過找到特定文章的分佈進行排序之後，可以看到此主題的比重高的文章在討論什麼。

news_topic %>%
  arrange(desc(`檢疫狀況`)) %>%head(20)

news_topic %>%
  arrange(desc(`陳時中個人新聞`)) %>%head(20)

news_topic %>%
  arrange(desc(`疫苗接種`)) %>%head(20)

news_topic %>%
  arrange(desc(`美豬事件`)) %>%head(20)

了解主題在時間的變化

news_topic %>% 
  mutate(artDate = as.Date(artDate)) %>%
  group_by(artDate = format(artDate,'%Y%m')) %>%
  summarise_if(is.numeric, sum, na.rm = TRUE) %>%
  melt(id.vars = "artDate") %>%
  filter(variable != c("commentNum")) %>%
  filter(variable != c("push")) %>%
  filter(variable != c("boo")) %>%
  ggplot( aes(x=artDate, y=value, fill = variable)) +
  geom_bar(stat = "identity") + ylab("value") +
  scale_fill_manual(values=mycolors[c(1,3,5,7)])+
  theme(axis.text.x = element_text(angle = 90, hjust = 1))

以比例了解主題時間變化

news_topic %>%
  mutate(artDate = as.Date(artDate)) %>% 
  #filter( !format(artDate,'%Y%m') %in% c(202011,202105))%>%
  group_by(artDate = format(artDate,'%Y%m')) %>%
  summarise_if(is.numeric, sum, na.rm = TRUE) %>%
  melt(id.vars = "artDate")%>%
  filter(variable != c("commentNum")) %>%
  filter(variable != c("push")) %>%
  filter(variable != c("boo")) %>%
  group_by(artDate)%>%
  mutate(total_value =sum(value))%>%
  ggplot( aes(x=artDate, y=value/total_value, fill=variable)) + 
  geom_bar(stat = "identity") + ylab("proportion") + 
    scale_fill_manual(values=mycolors[c(1,3,5,7)])+
    theme(axis.text.x = element_text(angle = 90, hjust = 1))

# > topic 檢疫狀況:
#   皆佔有一定比例，唯獨2020 9月討論稍低，推測可能是疫情情況好轉且美豬事件討論度高
# 
# > topic 陳時中個人新聞:
#   2020 9月、2021 3月登小巨蛋開金嗓
#   2020 5月墾丁視察防疫行程
#   2020 3月兒子設計"洗手罷韓T"
# 
# > topic 疫苗接種:
#   2021 2月後討論度較高，購買 AZ、BNT 疫苗之討論
# 
# > topic 美豬事件:
#   2020 9,10,11月美豬事件討論度最高

補充 - 不同訓練LDA模型套件

參考 http://text2vec.org/topic_modeling.html#latent_dirichlet_allocation

library(text2vec)

## Warning: package 'text2vec' was built under R version 4.0.5

## 
## Attaching package: 'text2vec'

## The following object is masked from 'package:topicmodels':
## 
##     perplexity

library(udpipe)

## Warning: package 'udpipe' was built under R version 4.0.5

jieba_tokenizer = worker(user="word.txt",stop_word="stop_words.txt")
news_tokenizer <- function(t) {
  lapply(t, function(x) {
    if(nchar(x)>1){
      tokens <- segment(x, jieba_tokenizer)
      # 去掉字串長度爲1的詞彙
      tokens <- tokens[nchar(tokens)>1]
      return(tokens)
    }
  })
}

another_tokens <- metadata %>%
  unnest_tokens(word, sentence, token=news_tokenizer) %>%
  filter(!str_detect(word, regex("[0-9a-zA-Z]"))| str_detect(word, regex("[Aa][Zz]")))

建立DTM matrix

dtf <- document_term_frequencies(another_tokens, document = "artUrl", term = "word")
dtm <- document_term_matrix(x = dtf)
dtm_clean <- dtm_remove_lowfreq(dtm, minfreq = 30)
dim(dtm_clean)

## [1] 3546 2468

LDA 模型

set.seed(2019)

topic_n = 6

lda_model =text2vec::LDA$new(n_topics = topic_n,doc_topic_prior = 0.1, topic_word_prior = 0.001)
doc_topic_distr =lda_model$fit_transform(dtm_clean, n_iter = 1000, convergence_tol = 1e-5,check_convergence_every_n = 100)

## INFO  [18:37:03.552] early stopping at 250 iteration 
## INFO  [18:37:06.408] early stopping at 40 iteration

這個比topicmodels的package跑快超多倍

一樣可以用LDAvis的套件來看

lda_model$get_top_words(n = 10, lambda = 0.5) ## 查看 前10主題字

##       [,1]     [,2]     [,3]     [,4]         [,5]     [,6]  
##  [1,] "口罩"   "柯文哲" "衛福部" "疫苗"       "台灣"   "確診"
##  [2,] "疫情"   "市長"   "健保"   "美豬"       "防疫"   "採檢"
##  [3,] "戴口罩" "陳時中" "陳時中" "進口"       "陳時中" "個案"
##  [4,] "旅遊"   "民進黨" "紓困"   "標示"       "部長"   "隔離"
##  [5,] "肺炎"   "蘇貞昌" "討論"   "開放"       "疫情"   "醫院"
##  [6,] "日本"   "中央"   "政策"   "接種"       "網友"   "感染"
##  [7,] "國家"   "台北市" "爭議"   "豬肉"       "墾丁"   "檢疫"
##  [8,] "管制"   "侯友宜" "溝通"   "萊克多巴胺" "感謝"   "陰性"
##  [9,] "措施"   "政治"   "地方"   "瘦肉精"     "記者"   "症狀"
## [10,] "民眾"   "防疫"   "保費"   "施打"       "合作"   "檢驗"

lda_model$plot()
# lda_model$plot(out.dir ="lda_result", open.browser = TRUE)

使用主題模型分析 2020 年至今陳時中相關之事件

第8組-糠維軒張惠茹陳靖婷鄭宇謙張揚琛鄭周祥

2021/05/17

ch0.套件取得及資料載入

套件

資料描述

資料前處理

將資料轉換為Document Term Matrix (DTM)

ch2. 主題模型

建立LDA模型

利用LDA模型建立phi矩陣

尋找Topic的代表字

建立更多主題的主題模型

ch4. LDA分析

選定6個主題數的主題模型

主題命名

Document 主題分佈

查看特定主題的文章

了解主題在時間的變化

以比例了解主題時間變化

補充 - 不同訓練LDA模型套件

建立DTM matrix

LDA 模型

一樣可以用LDAvis的套件來看

使用主題模型分析 2020 年至今陳時中相關之事件

第8組-糠維軒 張惠茹 陳靖婷 鄭宇謙 張揚琛 鄭周祥

2021/05/17

ch0.套件取得及資料載入

套件

資料描述

資料前處理

將資料轉換為Document Term Matrix (DTM)

ch2. 主題模型

建立LDA模型

利用LDA模型建立phi矩陣

尋找Topic的代表字

建立更多主題的主題模型

ch4. LDA分析

選定6個主題數的主題模型

主題命名

Document 主題分佈

查看特定主題的文章

了解主題在時間的變化

以比例了解主題時間變化

補充 - 不同訓練LDA模型套件

建立DTM matrix

LDA 模型

一樣可以用LDAvis的套件來看

第8組-糠維軒張惠茹陳靖婷鄭宇謙張揚琛鄭周祥