View source for R: tidytext: document-term-matrices-mining financial articles

 # Ref: https://github.com/dgrtwo/tidy-text-mining/blob/master/05-document-term-matrices.Rmd

 library(tm.plugin.webmining)
 library(purrr)
 company <- c("Microsoft", "Apple", "Google", "Amazon", "Facebook",
              "Twitter", "IBM", "Yahoo", "Netflix")
 symbol <- c("MSFT", "AAPL", "GOOG", "AMZN", "FB", "TWTR", "IBM", "YHOO", "NFLX")
 download_articles <- function(symbol) {
   WebCorpus(GoogleFinanceSource(paste0("NASDAQ:", symbol)))
 }

 # ADA ERROR naga2-nya di function download article atau di tibble
 stock_articles <- tibble(company = company,
                          symbol = symbol) %>%
   mutate(corpus = map(symbol, download_articles)) 
 stock_articles



 # tokenized
 stock_tokens <- stock_articles %>%
   mutate(corpus = map(corpus, tidy)) %>%
   unnest(cols = (corpus)) %>%
   unnest_tokens(word, text) %>%
   select(company, datetimestamp, word, id, heading)
 stock_tokens


 # tf-idf
 library(stringr)
 stock_tf_idf <- stock_tokens %>%
   count(company, word) %>%
   filter(!str_detect(word, "\\d+")) %>%
   bind_tf_idf(word, company, n) %>%
   arrange(-tf_idf)

 # plot
 stock_tf_idf %>%
   group_by(company) %>%
   top_n(8, tf_idf) %>%
   ungroup() %>%
   mutate(word = reorder(word, tf_idf)) %>%
   ggplot(aes(word, tf_idf, fill = company)) +
   geom_col(show.legend = FALSE) +
   facet_wrap(~ company, scales = "free") +
   coord_flip() +
   labs(x = "Word",
        y = "tf-idf")

 # sentiment
 stock_tokens %>%
   anti_join(stop_words, by = "word") %>%
   count(word, id, sort = TRUE) %>%
   inner_join(get_sentiments("afinn"), by = "word") %>%
   group_by(word) %>%
   summarize(contribution = sum(n * value)) %>%
   top_n(12, abs(contribution)) %>%
   mutate(word = reorder(word, contribution)) %>%
   ggplot(aes(word, contribution)) +
   geom_col() +
   coord_flip() +
   labs(y = "Frequency of word * AFINN value") 


 stock_tokens %>%
   anti_join(stop_words, by = "word") %>%
   count(word, id, sort = TRUE) %>%
   inner_join(afinn, by = "word") %>%
   group_by(word) %>%
   summarize(contribution = sum(n * value)) %>%
   top_n(12, abs(contribution)) %>%
   mutate(word = reorder(word, contribution)) %>%
   ggplot(aes(word, contribution)) +
   geom_col() +
   coord_flip() +
   labs(y = "Frequency of word * AFINN value")



 stock_tokens %>%
   count(word) %>%
   inner_join(get_sentiments("loughran"), by = "word") %>%
   group_by(sentiment) %>%
   top_n(5, n) %>%
   ungroup() %>%
   mutate(word = reorder(word, n)) %>%
   ggplot(aes(word, n)) +
   geom_col() +
   coord_flip() +
   facet_wrap(~ sentiment, scales = "free") +
   ylab("Frequency of this word in the recent financial articles")


 stock_tokens %>%
   count(word) %>%
   inner_join(loughran, by = "word") %>%
   group_by(sentiment) %>%
   top_n(5, n) %>%
   ungroup() %>%
   mutate(word = reorder(word, n)) %>%
   ggplot(aes(word, n)) +
   geom_col() +
   coord_flip() +
   facet_wrap(~ sentiment, scales = "free") +
   ylab("Frequency of this word in the recent financial articles")


 # calculate sentiment
 stock_sentiment_count <- stock_tokens %>%
   inner_join(get_sentiments("loughran"), by = "word") %>%
   count(sentiment, company) %>%
   spread(sentiment, n, fill = 0)
 stock_sentiment_count


 stock_sentiment_count <- stock_tokens %>%
   inner_join(loughran, by = "word") %>%
   count(sentiment, company) %>%
   spread(sentiment, n, fill = 0)
 stock_sentiment_count


 stock_sentiment_count %>%
   mutate(score = (positive - negative) / (positive + negative)) %>%
   mutate(company = reorder(company, score)) %>%
   ggplot(aes(company, score, fill = score > 0)) +
   geom_col(show.legend = FALSE) +
   coord_flip() +
   labs(x = "Company",
        y = "Positivity score among 20 recent news articles")



==Referensi==

* https://github.com/dgrtwo/tidy-text-mining/blob/master/05-document-term-matrices.Rmd


==Pranala Menarik==

* [[R]]