A Frosty Deal?

textual analysis
word embeddings
natural language processing
Quantitative textual analysis, word embeddings and analysing shifting trade-talk sentiment?

Carl Goodwin


September 18, 2020


November 22, 2022

Before the post-Brexit trade negotiations concluded, what did quantitative textual analysis and word embeddings tell us about the shifting trade-talk sentiment?

Reading news articles on the will-they-won’t-they post-Brexit trade negotiations with the EU sees days of optimism jarred by days of gloom. Do negative news articles, when one wants a positive outcome, leave a deeper impression?

Is it possible to get a more objective view from quantitative analysis of textual data? To do this, I’m going to look at hundreds of articles published in the Guardian newspaper over the course of the year to see how trade-talk sentiment changed week-to-week.


(cols <- wes_palette(name = "Chevalier1"))

The Withdrawal Agreement between the UK and the European Union was signed on the 24th of January 2020. Brexit-related newspaper articles will be imported from that date.


Since publishing this article in September 2020, an agreement was reached on December 24th 2020.

The Guardian newspaper asks for requests to span no more than 1 month at a time. Creating a set of monthly date ranges will enable the requests to be chunked.

dates_df <- tibble(start_date = date_build(2020, 1:11, 25)) |> 
  mutate(end_date = add_months(start_date, 1) |> add_days(-1))

start_date end_date
2020-01-25 2020-02-24
2020-02-25 2020-03-24
2020-03-25 2020-04-24
2020-04-25 2020-05-24
2020-05-25 2020-06-24
2020-06-25 2020-07-24
2020-07-25 2020-08-24
2020-08-25 2020-09-24
2020-09-25 2020-10-24
2020-10-25 2020-11-24
2020-11-25 2020-12-24

Access to the Guardian’s API via guardianapi(Odell 2019) requires a key which may be requested here and stored in the .Renviron file.


read_slowly <- slowly(gu_content)

article_df <-
  pmap_dfr(dates_df, function(start_date, end_date) {
      from_date = start_date,
      to_date = end_date


The data need a little cleaning, for example, to remove multi-topic articles, html tags and non-breaking spaces.

trade_df <-
  article_df |>
  filter(!str_detect(id, "/live/"), 
         section_id %in% c("world", "politics", "business")) |>
    body = str_remove_all(body, "<.*?>") |> str_to_lower(),
    body = str_remove_all(body, "[^a-z0-9 .-]"),
    body = str_remove_all(body, "nbsp")

A corpus then gives me a collection of texts whereby each document is a newspaper article.

trade_corp <- trade_df |> 
  corpus(docid_field = "short_url", 
         text_field = "body", unique_docnames = FALSE)

Although only articles mentioning Brexit have been imported, some of these will not be related to trade negotiations with the EU. For example, there are on-going negotiations with many countries around the world. So, word embeddings(Selivanov, Bickel, and Wang 2022) will help to narrow the focus to the specific context of the UK-EU trade deal.

The chief negotiator for the EU is Michel Barnier, so I’ll quantitatively identify words in close proximity to “Barnier” in the context of these Brexit news articles.

window <- 5

trade_fcm <-
  trade_corp |>
  fcm(context = "window", window = window, 
      count = "weighted", weights = window:1)

glove <- GlobalVectors$new(rank = 60, x_max = 10)


wv_main <- glove$fit_transform(trade_fcm, n_iter = 10)
INFO  [08:42:44.383] epoch 1, loss 0.3820
INFO  [08:42:46.699] epoch 2, loss 0.2577
INFO  [08:42:49.027] epoch 3, loss 0.2292
INFO  [08:42:51.414] epoch 4, loss 0.2092
INFO  [08:42:53.866] epoch 5, loss 0.1923
INFO  [08:42:56.296] epoch 6, loss 0.1793
INFO  [08:42:58.640] epoch 7, loss 0.1695
INFO  [08:43:01.009] epoch 8, loss 0.1620
INFO  [08:43:03.414] epoch 9, loss 0.1558
INFO  [08:43:05.675] epoch 10, loss 0.1507
wv_context <- glove$components
word_vectors <- wv_main + t(wv_context)

search_coord <- 
  word_vectors["barnier", , drop = FALSE]

word_vectors |> 
  sim2(search_coord, method = "cosine") |> 
  as_tibble(rownames = NA) |> 
  rownames_to_column("term") |> 
  rename(similarity = 2) |> 
  slice_max(similarity, n = 10)
term similarity
barnier 1.0000000
michel 0.8263884
negotiator 0.8200934
frost 0.8180818
negotiators 0.7154735
brussels 0.7154449
chief 0.6610545
team 0.6500327
friday 0.6436125
eus 0.6404557

Word embedding is a learned modelling technique placing words into a multi-dimensional vector space such that contextually-similar words may be found close by. Not surprisingly, one of the closest words contextually is “Michel”. And as he is the chief negotiator for the EU, we find “negotiator” and “brussels” also in the top most contextually-similar words.

The word embeddings algorithm, through word co-occurrence, has identified the name of Michel Barnier’s UK counterpart David Frost. So filtering articles for “Barnier”, “Frost” and “UK-EU” should help narrow the focus.

context_df <- 
  trade_df |> 
  filter(str_detect(body, "barnier|frost|uk-eu")) 

context_corp <- 
  context_df |> 
  corpus(docid_field = "short_url", text_field = "body")

Quanteda’s(Benoit et al. 2018) kwic function shows key phrases in context to ensure we’re homing in on the required texts. Short URLs are included below so one can click on any to read the actual article as presented by The Guardian.


context_corp |>
    remove_punct = TRUE,
    remove_symbols = TRUE,
    remove_numbers = TRUE
  ) |>
  kwic(pattern = phrase(c("trade negotiation", "trade deal", "trade talks")), 
       valuetype = "regex", window = 7) |>
  as_tibble() |>
  left_join(article_df, by = c("docname" = "short_url")) |> 
  slice_sample(n = 10) |> 
  select(docname, pre, keyword, post, headline)
docname pre keyword post headline
https://www.theguardian.com/p/ep2yb put pressure on brussels to agree a trade deal and iron out problems with the withdrawal Boris Johnson bows to Tory rebels with Brexit bill compromise
https://www.theguardian.com/p/dag4n the uk could not have the same trade deal with the eu as canada he said Brexit deal ‘a different ball game’ to Canada agreement, warns EU
https://www.theguardian.com/p/ekz7e has gone down badly in brussels in trade negotiations usually both sides work out a consolidated Barnier ‘flabbergasted’ at UK attempt to reopen Brexit specialty food debate
https://www.theguardian.com/p/fptbj for their companies this is the first trade deal in history that has been about erecting Brexit talks followed common pattern but barrier-raising outcome is unique
https://www.theguardian.com/p/dq896 text contains a cut-and-paste from the eus trade deal with canada stating merely that it would Brexit talks: Britain accuses EU of treating UK as ‘unworthy’ partner
https://www.theguardian.com/p/fmmga the conservative party for years john harris trade deals are not meant to assert sovereignty she EU leaders stress unity as they welcome Brexit trade talks extension
https://www.theguardian.com/p/fv2xh demanded a last-minute compromise to reach a trade deal and avert chaos at the border as Firms plead for Brexit deal as coronavirus leaves industry reeling
https://www.theguardian.com/p/f6444 canada-style trade deal the eu has a trade deal with canada called the comprehensive economic and What did Boris Johnson mean by an Australia-style system of trade?
https://www.theguardian.com/p/fk5kt companies await news of a potential uk-eu trade deal abf said our businesses have completed all Primark reports ‘phenomenal’ trading since lockdowns ended
https://www.theguardian.com/p/evgxe in talks trying to thrash out a trade deal before january but after the chief negotiators Wednesday briefing: Tory revolt over Cummings piles pressure on PM

Quanteda provides a sentiment dictionary which, in addition to identifying positive and negative words, also finds negative-negatives and negative-positives such as, for example, “not effective”. For each week’s worth of articles, we can calculate the proportion of positive sentiments.


sent_df <- 
  context_corp |> 
  tokens() |> 
  dfm(dictionary = data_dictionary_LSD2015) |> 
  as_tibble() |>
  left_join(context_df, by = c("doc_id" = "short_url")) |> 
    pos = positive + neg_negative,
    neg = negative + neg_positive,
    date = date_ceiling(as.Date(web_publication_date), "week"),
    pct_pos = pos / (pos + neg)

sent_df |> 
  select(Article = doc_id, "Pos Score" = pos, "Neg Score" = neg) |> 
Article Pos Score Neg Score
https://www.theguardian.com/p/d6qhb 40 22
https://www.theguardian.com/p/d9e9j 27 15
https://www.theguardian.com/p/d6kzd 52 27
https://www.theguardian.com/p/d9vjq 13 23
https://www.theguardian.com/p/d6t3c 28 26
https://www.theguardian.com/p/d79cn 57 51
https://www.theguardian.com/p/d7n8b 57 35
https://www.theguardian.com/p/d9xtf 33 14
https://www.theguardian.com/p/dag4n 37 38
https://www.theguardian.com/p/d7d9t 23 11
summary_df <- sent_df |> 
  group_by(date) |> 
  summarise(pct_pos = mean(pct_pos), n = n())

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Plotting the changing proportion of positive sentiment over time did surprise me a little. The outcome was more balanced than I expected which perhaps confirms the deeper impression left on me by negative articles.

The upper plot shows a rolling 7-day mean with a narrowing ribbon representing a narrowing variation in sentiment.

The lower plot shows the volume of articles. As we draw closer to the crunch-point the volume appears to be picking up.

width <- 7

sent_df2 <- sent_df |>
  mutate(web_date = as.Date(web_publication_date)) |> 
  group_by(web_date) |>
  summarise(pct_pos = sum(pos) / sum(neg + pos)) |> 
    roll_mean = slide_dbl(pct_pos, mean, .before = 6),
    roll_lq = slide_dbl(pct_pos, ~ quantile(.x, probs = 0.25), .before = 6),
    roll_uq = slide_dbl(pct_pos, ~ quantile(.x, probs = 0.75), .before = 6)

p1 <- sent_df2 |>
  ggplot(aes(web_date)) +
  geom_line(aes(y = roll_mean), colour = cols[1]) +
  geom_ribbon(aes(ymin = roll_lq, ymax = roll_uq), 
              alpha = 0.33, fill = cols[1]) +
  geom_hline(yintercept = 0.5, linetype = "dashed", 
             colour = cols[4], linewidth = 1) +
  scale_y_continuous(labels = label_percent(accuracy = 1)) +
    title = "Changing Sentiment Towards a UK-EU Trade Deal",
    subtitle = glue("Rolling {width} days Since the Withdrawal Agreement"),
    x = NULL, y = "Positive Sentiment"

p2 <- summary_df |> 
  ggplot(aes(date, n)) +
  geom_line(colour = cols[1]) +
  labs(x = "Weeks", y = "Article Count",
       caption = "Source: Guardian Newspaper")

p1 / p2 + 
  plot_layout(heights = c(2, 1))

Some writers exhibit more sentiment variation than others.

byline_df <- 
  sent_df |> 
  mutate(byline = word(byline, 1, 2) |> str_remove_all("[[:punct:]]")) |> 
  group_by(byline, date) |> 
  summarise(pct_pos = mean(pct_pos), n = n()) |> 

top_3 <- byline_df |> 
  count(byline, sort = TRUE) |> 
  slice_head(n = 3) |> 

byline_df |> 
  filter(byline %in% top_3) |> 
  ggplot(aes(date, pct_pos, colour = byline)) +
  geom_line() +
  geom_hline(yintercept = 0.5, linetype = "dotted", colour = cols[2]) +
  scale_y_continuous(labels = label_percent(), limits = c(0.2, 0.8)) +
  scale_colour_manual(values = cols[c(1, 2, 4)]) +
  labs(title = "Changing Sentiment Towards a UK-EU Trade Deal",
       subtitle = "Three Selected Bylines",
       x = "Weeks", y = "Positive Sentiment", colour = "Byline", 
       caption = "Source: The Guardian")

R Toolbox

Summarising below the packages and functions used in this post enables me to separately create a toolbox visualisation summarising the usage of packages and functions across all posts.

Package Function
base c[6]; sum[3]; as.Date[2]; function[2]; mean[2]; set.seed[2]; conflicts[1]; cumsum[1]; search[1]; t[1]
clock add_days[1]; add_months[1]; date_build[1]; date_ceiling[1]
dplyr mutate[10]; filter[8]; group_by[4]; summarise[4]; if_else[3]; n[3]; left_join[2]; select[2]; arrange[1]; count[1]; desc[1]; pull[1]; rename[1]; slice[1]; slice_head[1]; slice_max[1]; slice_sample[1]; ungroup[1]
ggplot2 aes[5]; geom_line[3]; ggplot[3]; labs[3]; geom_hline[2]; scale_y_continuous[2]; geom_ribbon[1]; scale_colour_manual[1]; theme_bw[1]; theme_set[1]
glue glue[1]
guardianapi gu_content[1]
patchwork plot_layout[1]
purrr map[1]; map2_dfr[1]; pmap_dfr[1]; possibly[1]; set_names[1]; slowly[1]
quanteda corpus[2]; tokens[2]; data_dictionary_LSD2015[1]; dfm[1]; fcm[1]; kwic[1]; phrase[1]
readr read_lines[1]
scales label_percent[2]
slider slide_dbl[3]
stats quantile[2]
stringr str_c[5]; str_remove_all[5]; str_detect[4]; str_remove[2]; str_count[1]; str_starts[1]; str_to_lower[1]; word[1]
text2vec sim2[1]
tibble as_tibble[4]; tibble[3]; enframe[1]; rownames_to_column[1]
tictoc tic[2]; toc[2]
tidyr unnest[1]
wesanderson wes_palette[1]


Benoit, Kenneth, Kohei Watanabe, Haiyan Wang, Paul Nulty, Adam Obeng, Stefan Müller, and Akitaka Matsuo. 2018. “Quanteda: An r Package for the Quantitative Analysis of Textual Data” 3: 774. https://doi.org/10.21105/joss.00774.
Odell, Evan. 2019. Guardianapi: Access the ’Guardian’ Newspaper Open Data API.” https://doi.org/10.5281/zenodo.2551001.
Selivanov, Dmitriy, Manuel Bickel, and Qing Wang. 2022. “Text2vec: Modern Text Mining Framework for r.” https://CRAN.R-project.org/package=text2vec.