Difference between revisions of "R: tidytext: document-term-matrices-mining financial articles"

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(Created page with " # 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",...")
 
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Latest revision as of 18:09, 4 December 2019

# 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")


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