We’ll get started on these today – sentiment and readability
We’ll cover topic modeling next session
This is what we will work on today, and we will revist some of this in the remaining class sessions
Wide format
## # A tibble: 3 x 3
## quarter level_3 value
## <chr> <chr> <chr>
## 1 1995-Q1 Wholesale Trade 17
## 2 1995-Q1 Retail Trade -18
## 3 1995-Q1 Accommodation 16
Long format
## # A tibble: 3 x 4
## RegionID `1996-04` `1996-05` `1996-06`
## <dbl> <dbl> <dbl> <dbl>
## 1 84654 334200 335400 336500
## 2 90668 235700 236900 236700
## 3 91982 210400 212200 212200
The structure is given by the IDs, dates, and variables
"JANUARY"
, "ONE"
, "FEMALE"
All of these require us to determine and impose structure
What are call centers using NLP for?
How does NLP help call centers with their business?
Where an we make use of NLP in business?
\ | ( ) [ { } ^ $ * + ? . !
\
\
is a special character, we’ll need to put \
before \
…
$
, we would use \\$
\t
is tab\r
is newline (files from Macs)\r\n
is newline (files from Windows)\n
is newline (files from Unix, Linux, etc.)\\
to get the backslashes though# Read text from a .txt file using read_file()
doc <- read_file("../../Data/0001104659-14-015152.txt")
# str_wrap is from stringr from tidyverse
cat(str_wrap(substring(doc,1,500), 80))
## UNITED STATES SECURITIES AND EXCHANGE COMMISSION WASHINGTON, D.C. 20549 FORM
## 10-K ANNUAL REPORT PURSUANT TO SECTION 13 OR 15(d) OF THE SECURITIES EXCHANGE
## ACT OF 1934 For the fiscal year ended December 31, 2013 Commission file number
## 1-9924 Citigroup Inc. (Exact name of registrant as specified in its charter)
## Securities registered pursuant to Section 12(b) of the Act: See Exhibit 99.01
## Securities registered pursuant to Section 12(g) of the Act: none Indicate by
## check mark if the registrant is a
<a>
, <table>
, <img>
, etc.library(httr)
library(XML)
httpResponse <- GET('https://coinmarketcap.com/currencies/ethereum/')
html = httr::content(httpResponse, "text")
paste0('...', str_wrap(substring(html, 135418, 135480), 80), '...')
## [1] "...div>$349.36 USD /</div><div>$352.16 USD</div></td></tr><tr><th..."
xpath <- '//*[@id="__next"]/div[1]/div[2]/div[1]/div[2]/div[1]/div/div[2]/span[1]/span[1]/text()'
hdoc = htmlParse(html, asText=TRUE) # from XML
price <- xpathSApply(hdoc, xpath, xmlValue)
print(paste0("Ethereum was priced at $", price,
" when these slides were compiled"))
## [1] "Ethereum was priced at $$349.39 when these slides were compiled"
# The actual version I use (with caching to avoid repeated lookups) is in the appendix
cryptoMC <- function(name) {
httpResponse <- GET(paste('https://coinmarketcap.com/currencies/',name,'/',sep=''))
xpath <- '//*[@id="__next"]/div[1]/div[2]/div[1]/div[2]/div[1]/div/div[2]/span[1]/span[1]/text()'
hdoc = htmlParse(html, asText=TRUE)
plain.text <- xpathSApply(hdoc, xpath, xmlValue)
plain.text
}
## [1] "Ethereum was priced at $349.39"
## [1] "Litecoin was priced at $349.39"
We will cover these using stringr as opposed to base R – stringr’s commands are much more consistent
## Citis net income was $13.5 billion
## Net income decreased 14%, mainly driven by lower revenues and lower loan loss
## reserve releases, partially offset by lower net credit losses and expenses.
sep=
optioncollapse=
with the desired separatorsep=""
## [1] "citis net income was $13.5 billion"
## [1] "CITIS NET INCOME WAS $13.5 BILLION"
## [1] "Citis Net Income Was $13.5 Billion"
str_
prefixed functions support non-English languages as wellpaste
# board is a list of director names
# titles is a list of the director's titles
paste(board, titles, sep=", ")
## [1] "Michael L. Corbat, CEO"
## [2] "Michael E. O’Neill, Chairman"
## [3] "Anthony M. Santomero, Former president, Fed (Philidelphia)"
## [4] "William S. Thompson, Jr., CEO, Retired, PIMCO"
## [5] "Duncan P. Hennes, Co-Founder/Partner, Atrevida Partners"
## [6] "Gary M. Reiner, Operating Partner, General Atlantic"
## [7] "Joan E. Spero, Senior Research Scholar, Columbia University"
## [8] "James S. Turley, Former Chairman & CEO, E&Y"
## [9] "Franz B. Humer, Chairman, Roche"
## [10] "Judith Rodin, President, Rockefeller Foundation"
## [11] "Robert L. Ryan, CFO, Retired, Medtronic"
## [12] "Diana L. Taylor, MD, Wolfensohn Fund Management"
## [13] "Ernesto Zedillo Ponce de Leon, Professor, Yale University"
## [14] "Robert L. Joss, Professor/Dean Emeritus, Stanford GSB"
cat(str_wrap(paste0("Citi's board consists of: ",
paste(board[1:length(board)-1], collapse=", "),
", and ", board[length(board)], "."), 80))
## Citi's board consists of: Michael L. Corbat, Michael E. O’Neill, Anthony M.
## Santomero, William S. Thompson, Jr., Duncan P. Hennes, Gary M. Reiner, Joan E.
## Spero, James S. Turley, Franz B. Humer, Judith Rodin, Robert L. Ryan, Diana L.
## Taylor, Ernesto Zedillo Ponce de Leon, and Robert L. Joss.
## [1] "Citis net income was $13.5 billion"
## [1] "Citis net income was over $10 billion"
[[1]]
can extract the first vectorn=
n=
## [1] 206
## The total amount of securities authorized pursuant to any instrument defining
## rights of holders of long-term debt of the Company does not exceed 10% of the
## total assets of the Company and its consolidated subsidiaries. The Company
## will furnish copies of any such instrument to the SEC upon request. Copies of
## any of the exhibits referred to above will be furnished at a cost of $0.25 per
## page (although no charge will be made for the 2013 Annual Report on Form 10-
## K) to security holders who make written request to Citigroup Inc., Corporate
## Governance, 153 East 53 rd Street, 19 th Floor, New York, New York 10022. *
## Denotes a management contract or compensatory plan or arrangement. + Filed
## herewith.
## [[1]]
## start end
## [1,] 8508 8517
## [2,] 9902 9911
## [3,] 16549 16558
## [4,] 17562 17571
## [5,] 28900 28909
## [6,] 32197 32206
## [7,] 35077 35086
## [8,] 37252 37261
## [9,] 40187 40196
## [10,] 43257 43266
## [11,] 45345 45354
## [12,] 47618 47627
## [13,] 51865 51874
## [14,] 51953 51962
## [15,] 52663 52672
## [16,] 52748 52757
## [17,] 54970 54979
## [18,] 58817 58826
## [19,] 96022 96031
## [20,] 96717 96726
## [21,] 99297 99306
## [22,] 188340 188349
## [23,] 189049 189058
## [24,] 201462 201471
## [25,] 456097 456106
## [26,] 460158 460167
## [27,] 460446 460455
## [28,] 460467 460476
## [29,] 475016 475025
## [30,] 475298 475307
## [31,] 545581 545590
## [32,] 554362 554371
TRUE
or FALSE
for the presence of a string in the text## [1] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE
## [1] 13
## [1] 0 0 0 0 0 4 0 0 2 2
## [1] 4
## start end
## [1,] 8508 8517
## [1] "net income"
@
sign.
in it[:graph:]+
.
.
though[:alnum:]+\\.[.[:alnum:]]+
# Extract all emails from the annual report
str_extract_all(doc,'[:graph:]+@[:alnum:]+\\.[.[:alnum:]]+')
## [[1]]
## [1] "shareholder@computershare.com" "shareholder@computershare.com"
## [3] "docserve@citi.com" "shareholderrelations@citi.com"
@
was itself – it isn’t a special character in strings in R\\.
is just a period – we need to escape .
because it is special[: :]
, is a set of characters
[:graph:]
means any letter, number, or punctuation[:alnum:]
means any letter or number+
is used to indicate that we want 1 or more of the preceding element – as many as it can match
[:graph:]+
meant “Give us every letter, number, and punctuation you can, but make sure there is at least 1.”[ ]
, ask for anything inside
[.[:alnum:]]+
meant “Give us every letter, number, and .
you can, but make sure there is at least 1.”shareholder@computershare.com
[:graph:]+@[:alnum:]+\\.[.[:alnum:]]+
[:graph:]+
\(\Rightarrow\) shareholder
@
\(\Rightarrow\) @
[:alnum:]+
\(\Rightarrow\) computershare
\\.
\(\Rightarrow\) .
[.[:alnum:]]+
\(\Rightarrow\) com
.
matches everything[:alpha:]
matches all letters[:lower:]
matches all lowercase letters[:upper:]
matches all UPPERCASE letters[:digit:]
matches all numbers 0 through 9[:alnum:]
matches all letters and numbers[:punct:]
matches all punctuation[:graph:]
matches all letters, numbers, and punctuation[:space:]
or \s
match ANY whitespace
\S
is the exact opposite[:blank:]
matches whitespace except newlinestext <- c("abcde", 'ABCDE', '12345', '!?!?.', 'ABC123?', "With space", "New\nline")
html_df(data.frame(
text=text,
alpha=str_detect(text,'[:alpha:]'),
lower=str_detect(text,'[:lower:]'),
upper=str_detect(text,'[:upper:]'),
digit=str_detect(text,'[:digit:]'),
alnum=str_detect(text,'[:alnum:]')
))
text | alpha | lower | upper | digit | alnum |
---|---|---|---|---|---|
abcde | TRUE | TRUE | FALSE | FALSE | TRUE |
ABCDE | TRUE | FALSE | TRUE | FALSE | TRUE |
12345 | FALSE | FALSE | FALSE | TRUE | TRUE |
!?!?. | FALSE | FALSE | FALSE | FALSE | FALSE |
ABC123? | TRUE | FALSE | TRUE | TRUE | TRUE |
With space | TRUE | TRUE | TRUE | FALSE | TRUE |
New line | TRUE | TRUE | TRUE | FALSE | TRUE |
text <- c("abcde", 'ABCDE', '12345', '!?!?.', 'ABC123?', "With space", "New\nline")
html_df(data.frame(
text=text,
punct=str_detect(text,'[:punct:]'),
graph=str_detect(text,'[:graph:]'),
space=str_detect(text,'[:space:]'),
blank=str_detect(text,'[:blank:]'),
period=str_detect(text,'.')
))
text | punct | graph | space | blank | period |
---|---|---|---|---|---|
abcde | FALSE | TRUE | FALSE | FALSE | TRUE |
ABCDE | FALSE | TRUE | FALSE | FALSE | TRUE |
12345 | FALSE | TRUE | FALSE | FALSE | TRUE |
!?!?. | TRUE | TRUE | FALSE | FALSE | TRUE |
ABC123? | TRUE | TRUE | FALSE | FALSE | TRUE |
With space | FALSE | TRUE | TRUE | TRUE | TRUE |
New line | FALSE | TRUE | TRUE | FALSE | TRUE |
[ ]
can be used to create a class of characters to look for
[abc]
matches anything that is a
, b
, c
[^ ]
can be used to create a class of everything else
[^abc]
matches anything that isn’t a
, b
, or c
x
is some element
x?
looks for 0 or 1 of x
x*
looks for 0 or more of x
x+
looks for 1 or more of x
x{n}
looks for n
(a number) of x
x{n, }
looks for at least n
of x
x{n,m}
looks for at least n
and at most m
of x
?
to any quantity operator to make it prefer the shortest match possible^
indicates the start of the string$
indicates the end of the string( )
can be used to group components|
can be used within groups as a logical or\\1
refers to the first group\\2
refers to the second group## [1] "STADLAUER MALZFABRIK" "JOAO FORTES ENGENHARIA SA"
## [1] "FGP LTD" "MBK PCL" "MYP LTD" "MCT BHD" "R T C L LTD"
## [1] "INTERNATIONAL ENTERTAINMENT" "PREMIERE HORIZON ALLIANCE"
## [3] "JOAO FORTES ENGENHARIA SA" "OVERSEAS CHINESE TOWN (ASIA)"
## [5] "COOPERATIVE CONSTRUCTION CO" "FRANCE TOURISME IMMOBILIER"
## [7] "BONEI HATICHON CIVIL ENGINE"
# Real estate firm names with a repeated 4 letter pattern
str_subset(RE_names, '([:upper:]{4}).*\\1')
## [1] "INTERNATIONAL ENTERTAINMENT" "CHONG HONG CONSTRUCTION CO"
## [3] "ZHONGHONG HOLDING CO LTD" "DEUTSCHE GEOTHERMISCHE IMMOB"
[12][90][:digit:][:digit:]
[01][:digit:]
[0123][:digit:]
Cavaet: Regexes are generally slow. If you can code something to avoid them, that is often better. But often that may be infeasible.
str_*()
functions use regex by default, they actually have four modes
boundary("word")
# Compustat firm names example
df_RE_names <- df_RE %>%
group_by(isin) %>%
slice(1) %>%
mutate(SG_in_name = str_detect(conm, "(SG|SINGAPORE)"),
name_length = str_length(conm),
SG_firm = ifelse(fic=="SGP",1,0)) %>%
ungroup()
df_RE_names %>%
group_by(SG_firm) %>%
mutate(pct_SG = mean(SG_in_name) * 100) %>%
slice(1) %>%
ungroup() %>%
select(SG_firm, pct_SG)
## # A tibble: 2 x 2
## SG_firm pct_SG
## <dbl> <dbl>
## 1 0 0.369
## 2 1 4.76
\[ 0.39 \left(\frac{\#~words}{\#~sentences}\right) + 11.8\left(\frac{\#~syllables}{\#~words}\right) - 15.59 \]
## document Flesch.Kincaid
## 1 text1 17.685
\[ \begin{aligned} & \left[ Mean(Words~per~sentence) +\right.\\ &\left.(\%~of~words~>3~syllables)\right] \times 0.4 \end{aligned} \]
## document FOG
## 1 text1 21.7582
\[ 5.88\left(\frac{\#~letters}{\#~words}\right)-29.6\left(\frac{\#~sentences}{\#~words}\right)-15.8 \]
## document Coleman.Liau.short
## 1 text1 14.96773
to_lower=FALSE
to avoid this if neededID | word | |
---|---|---|
1 | 0001104659-14-015152 | united |
1.1 | 0001104659-14-015152 | states |
1.2 | 0001104659-14-015152 | securities |
1.3 | 0001104659-14-015152 | and |
1.4 | 0001104659-14-015152 | exchange |
1.5 | 0001104659-14-015152 | commission |
token=
)
n=
)stopword
package to remove stopwords# get a list of stopwords
stop_en <- stopwords::stopwords("english") # Snowball English
paste0(length(stop_en), " words: ", paste(stop_en[1:5], collapse=", "))
## [1] "175 words: i, me, my, myself, we"
stop_SMART <- stopwords::stopwords(source="smart") # SMART English
paste0(length(stop_SMART), " words: ", paste(stop_SMART[1:5], collapse=", "))
## [1] "571 words: a, a's, able, about, above"
stop_fr <- stopwords::stopwords("french") # Snowball French
paste0(length(stop_fr), " words: ", paste(stop_fr[1:5], collapse=", "))
## [1] "164 words: au, aux, avec, ce, ces"
## Joining, by = "word"
## [1] 128728
## [1] 74985
terms <- df_doc_stop %>%
count(ID, word, sort=TRUE) %>%
ungroup()
total_terms <- terms %>%
group_by(ID) %>%
summarize(total = sum(n))
tf <- left_join(terms, total_terms) %>% mutate(tf=n/total)
tf
## ID word n total tf
## 1 0001104659-14-015152 citi 826 74985 0.011015536
## 2 0001104659-14-015152 2013 743 74985 0.009908648
## 3 0001104659-14-015152 credit 704 74985 0.009388544
## 4 0001104659-14-015152 citis 660 74985 0.008801760
## 5 0001104659-14-015152 risk 624 74985 0.008321664
## 6 0001104659-14-015152 december 523 74985 0.006974728
## 7 0001104659-14-015152 financial 513 74985 0.006841368
## 8 0001104659-14-015152 31 505 74985 0.006734680
## 9 0001104659-14-015152 loans 495 74985 0.006601320
## 10 0001104659-14-015152 assets 488 74985 0.006507968
## 11 0001104659-14-015152 fair 453 74985 0.006041208
## 12 0001104659-14-015152 securities 440 74985 0.005867840
## 13 0001104659-14-015152 billion 435 74985 0.005801160
## 14 0001104659-14-015152 citigroup 420 74985 0.005601120
## 15 0001104659-14-015152 2012 412 74985 0.005494432
## 16 0001104659-14-015152 u.s 390 74985 0.005201040
## 17 0001104659-14-015152 interest 373 74985 0.004974328
## 18 0001104659-14-015152 company 371 74985 0.004947656
## 19 0001104659-14-015152 net 324 74985 0.004320864
## 20 0001104659-14-015152 related 323 74985 0.004307528
## 21 0001104659-14-015152 capital 318 74985 0.004240848
## 22 0001104659-14-015152 million 316 74985 0.004214176
## 23 0001104659-14-015152 market 297 74985 0.003960792
## 24 0001104659-14-015152 consolidated 294 74985 0.003920784
## 25 0001104659-14-015152 cash 292 74985 0.003894112
## 26 0001104659-14-015152 including 279 74985 0.003720744
## 27 0001104659-14-015152 loan 271 74985 0.003614056
## 28 0001104659-14-015152 loss 267 74985 0.003560712
## 29 0001104659-14-015152 losses 265 74985 0.003534040
## 30 0001104659-14-015152 income 260 74985 0.003467360
## 31 0001104659-14-015152 year 253 74985 0.003374008
## 32 0001104659-14-015152 impact 247 74985 0.003293992
## 33 0001104659-14-015152 based 246 74985 0.003280656
## 34 0001104659-14-015152 1 245 74985 0.003267320
## 35 0001104659-14-015152 rate 233 74985 0.003107288
## 36 0001104659-14-015152 approximately 232 74985 0.003093952
## 37 0001104659-14-015152 regulatory 216 74985 0.002880576
## 38 0001104659-14-015152 transactions 215 74985 0.002867240
## 39 0001104659-14-015152 banking 213 74985 0.002840568
## 40 0001104659-14-015152 significant 212 74985 0.002827232
## 41 0001104659-14-015152 debt 211 74985 0.002813896
## 42 0001104659-14-015152 generally 205 74985 0.002733880
## 43 0001104659-14-015152 due 202 74985 0.002693872
## 44 0001104659-14-015152 information 202 74985 0.002693872
## 45 0001104659-14-015152 investment 197 74985 0.002627192
## 46 0001104659-14-015152 tax 196 74985 0.002613856
## 47 0001104659-14-015152 management 195 74985 0.002600520
## 48 0001104659-14-015152 mortgage 194 74985 0.002587184
## 49 0001104659-14-015152 business 189 74985 0.002520504
## 50 0001104659-14-015152 equity 187 74985 0.002493832
## 51 0001104659-14-015152 subject 186 74985 0.002480496
## 52 0001104659-14-015152 trading 185 74985 0.002467160
## 53 0001104659-14-015152 statements 184 74985 0.002453824
## 54 0001104659-14-015152 asset 182 74985 0.002427152
## 55 0001104659-14-015152 foreign 176 74985 0.002347136
## 56 0001104659-14-015152 liquidity 172 74985 0.002293792
## 57 0001104659-14-015152 table 168 74985 0.002240448
## 58 0001104659-14-015152 consumer 167 74985 0.002227112
## 59 0001104659-14-015152 basel 166 74985 0.002213776
## 60 0001104659-14-015152 portfolio 165 74985 0.002200440
## 61 0001104659-14-015152 primarily 164 74985 0.002187104
## 62 0001104659-14-015152 additional 162 74985 0.002160432
## 63 0001104659-14-015152 citigroups 161 74985 0.002147096
## 64 0001104659-14-015152 term 161 74985 0.002147096
## 65 0001104659-14-015152 derivative 160 74985 0.002133760
## 66 0001104659-14-015152 total 160 74985 0.002133760
## 67 0001104659-14-015152 liabilities 159 74985 0.002120424
## 68 0001104659-14-015152 required 159 74985 0.002120424
## 69 0001104659-14-015152 operations 155 74985 0.002067080
## 70 0001104659-14-015152 increased 154 74985 0.002053744
## 71 0001104659-14-015152 addition 153 74985 0.002040408
## 72 0001104659-14-015152 rates 153 74985 0.002040408
## 73 0001104659-14-015152 amount 148 74985 0.001973728
## 74 0001104659-14-015152 balance 148 74985 0.001973728
## 75 0001104659-14-015152 derivatives 147 74985 0.001960392
## 76 0001104659-14-015152 level 147 74985 0.001960392
## 77 0001104659-14-015152 corporate 146 74985 0.001947056
## 78 0001104659-14-015152 quarter 143 74985 0.001907048
## 79 0001104659-14-015152 companys 139 74985 0.001853704
## 80 0001104659-14-015152 iii 139 74985 0.001853704
## 81 0001104659-14-015152 reserve 138 74985 0.001840368
## 82 0001104659-14-015152 accounting 137 74985 0.001827032
## 83 0001104659-14-015152 period 137 74985 0.001827032
## 84 0001104659-14-015152 businesses 136 74985 0.001813696
## 85 0001104659-14-015152 instruments 136 74985 0.001813696
## 86 0001104659-14-015152 requirements 132 74985 0.001760352
## 87 0001104659-14-015152 result 132 74985 0.001760352
## 88 0001104659-14-015152 activities 130 74985 0.001733680
## 89 0001104659-14-015152 recorded 129 74985 0.001720344
## 90 0001104659-14-015152 sale 129 74985 0.001720344
## 91 0001104659-14-015152 services 129 74985 0.001720344
## 92 0001104659-14-015152 lower 128 74985 0.001707008
## 93 0001104659-14-015152 reporting 128 74985 0.001707008
## 94 0001104659-14-015152 results 128 74985 0.001707008
## 95 0001104659-14-015152 global 127 74985 0.001693672
## 96 0001104659-14-015152 stock 126 74985 0.001680336
## 97 0001104659-14-015152 note 125 74985 0.001667000
## 98 0001104659-14-015152 current 124 74985 0.001653664
## 99 0001104659-14-015152 exchange 124 74985 0.001653664
## 100 0001104659-14-015152 offset 123 74985 0.001640328
## 101 0001104659-14-015152 earnings 122 74985 0.001626992
## 102 0001104659-14-015152 potential 122 74985 0.001626992
## 103 0001104659-14-015152 revenues 122 74985 0.001626992
## 104 0001104659-14-015152 bank 121 74985 0.001613656
## 105 0001104659-14-015152 include 120 74985 0.001600320
## 106 0001104659-14-015152 future 119 74985 0.001586984
## 107 0001104659-14-015152 included 118 74985 0.001573648
## 108 0001104659-14-015152 markets 117 74985 0.001560312
## 109 0001104659-14-015152 collateral 115 74985 0.001533640
## 110 0001104659-14-015152 funding 115 74985 0.001533640
## 111 0001104659-14-015152 currency 114 74985 0.001520304
## 112 0001104659-14-015152 investments 114 74985 0.001520304
## 113 0001104659-14-015152 compared 113 74985 0.001506968
## 114 0001104659-14-015152 prior 113 74985 0.001506968
## 115 0001104659-14-015152 3 111 74985 0.001480296
## 116 0001104659-14-015152 citibank 111 74985 0.001480296
## 117 0001104659-14-015152 issued 111 74985 0.001480296
## 118 0001104659-14-015152 risks 111 74985 0.001480296
## 119 0001104659-14-015152 2014 110 74985 0.001466960
## 120 0001104659-14-015152 exposure 110 74985 0.001466960
## 121 0001104659-14-015152 federal 110 74985 0.001466960
## 122 0001104659-14-015152 revenue 109 74985 0.001453624
## 123 0001104659-14-015152 awards 105 74985 0.001400280
## 124 0001104659-14-015152 basis 105 74985 0.001400280
## 125 0001104659-14-015152 costs 105 74985 0.001400280
## 126 0001104659-14-015152 higher 105 74985 0.001400280
## 127 0001104659-14-015152 years 105 74985 0.001400280
## 128 0001104659-14-015152 common 104 74985 0.001386944
## 129 0001104659-14-015152 flows 104 74985 0.001386944
## 130 0001104659-14-015152 long 104 74985 0.001386944
## 131 0001104659-14-015152 recognized 104 74985 0.001386944
## 132 0001104659-14-015152 rules 104 74985 0.001386944
## 133 0001104659-14-015152 sheet 104 74985 0.001386944
## 134 0001104659-14-015152 claims 102 74985 0.001360272
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## 3851 0001104659-14-015152 23.0 1 74985 0.000013336
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## 3938 0001104659-14-015152 319 1 74985 0.000013336
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## 5451 0001104659-14-015152 transmission 1 74985 0.000013336
## 5452 0001104659-14-015152 travelers 1 74985 0.000013336
## 5453 0001104659-14-015152 treasurers 1 74985 0.000013336
## 5454 0001104659-14-015152 treats 1 74985 0.000013336
## 5455 0001104659-14-015152 treaty 1 74985 0.000013336
## 5456 0001104659-14-015152 tribunals 1 74985 0.000013336
## 5457 0001104659-14-015152 triggering 1 74985 0.000013336
## 5458 0001104659-14-015152 true 1 74985 0.000013336
## 5459 0001104659-14-015152 trustees 1 74985 0.000013336
## 5460 0001104659-14-015152 trustscitibank 1 74985 0.000013336
## 5461 0001104659-14-015152 trustsmaster 1 74985 0.000013336
## 5462 0001104659-14-015152 turkish 1 74985 0.000013336
## 5463 0001104659-14-015152 turmoil 1 74985 0.000013336
## 5464 0001104659-14-015152 unaccrued 1 74985 0.000013336
## 5465 0001104659-14-015152 unadjusted 1 74985 0.000013336
## 5466 0001104659-14-015152 unavailability 1 74985 0.000013336
## 5467 0001104659-14-015152 unavailable 1 74985 0.000013336
## 5468 0001104659-14-015152 uncapped 1 74985 0.000013336
## 5469 0001104659-14-015152 uncleared 1 74985 0.000013336
## 5470 0001104659-14-015152 uncollectable 1 74985 0.000013336
## 5471 0001104659-14-015152 uncollectible 1 74985 0.000013336
## 5472 0001104659-14-015152 uncommitted 1 74985 0.000013336
## 5473 0001104659-14-015152 underserved 1 74985 0.000013336
## 5474 0001104659-14-015152 undersigned 1 74985 0.000013336
## 5475 0001104659-14-015152 understand 1 74985 0.000013336
## 5476 0001104659-14-015152 undertake 1 74985 0.000013336
## 5477 0001104659-14-015152 undisclosed 1 74985 0.000013336
## 5478 0001104659-14-015152 undrawn 1 74985 0.000013336
## 5479 0001104659-14-015152 unfavorable 1 74985 0.000013336
## 5480 0001104659-14-015152 unforeseen 1 74985 0.000013336
## 5481 0001104659-14-015152 unhedged 1 74985 0.000013336
## 5482 0001104659-14-015152 uniform 1 74985 0.000013336
## 5483 0001104659-14-015152 unlike 1 74985 0.000013336
## 5484 0001104659-14-015152 unparalleled 1 74985 0.000013336
## 5485 0001104659-14-015152 unpredictability 1 74985 0.000013336
## 5486 0001104659-14-015152 unrelated 1 74985 0.000013336
## 5487 0001104659-14-015152 unscheduled 1 74985 0.000013336
## 5488 0001104659-14-015152 unsecuritized 1 74985 0.000013336
## 5489 0001104659-14-015152 unstable 1 74985 0.000013336
## 5490 0001104659-14-015152 unsuitable 1 74985 0.000013336
## 5491 0001104659-14-015152 untested 1 74985 0.000013336
## 5492 0001104659-14-015152 unwilling 1 74985 0.000013336
## 5493 0001104659-14-015152 unwillingness 1 74985 0.000013336
## 5494 0001104659-14-015152 upcoming 1 74985 0.000013336
## 5495 0001104659-14-015152 updating 1 74985 0.000013336
## 5496 0001104659-14-015152 uploading 1 74985 0.000013336
## 5497 0001104659-14-015152 urgency 1 74985 0.000013336
## 5498 0001104659-14-015152 utilizations 1 74985 0.000013336
## 5499 0001104659-14-015152 vacate 1 74985 0.000013336
## 5500 0001104659-14-015152 vacated 1 74985 0.000013336
## 5501 0001104659-14-015152 vadas 1 74985 0.000013336
## 5502 0001104659-14-015152 vehicle 1 74985 0.000013336
## 5503 0001104659-14-015152 vendor 1 74985 0.000013336
## 5504 0001104659-14-015152 verbs 1 74985 0.000013336
## 5505 0001104659-14-015152 vertical 1 74985 0.000013336
## 5506 0001104659-14-015152 vi 1 74985 0.000013336
## 5507 0001104659-14-015152 viesbalance 1 74985 0.000013336
## 5508 0001104659-14-015152 viesliquidity 1 74985 0.000013336
## 5509 0001104659-14-015152 views 1 74985 0.000013336
## 5510 0001104659-14-015152 vintages 1 74985 0.000013336
## 5511 0001104659-14-015152 violated 1 74985 0.000013336
## 5512 0001104659-14-015152 virtually 1 74985 0.000013336
## 5513 0001104659-14-015152 viruses 1 74985 0.000013336
## 5514 0001104659-14-015152 visas 1 74985 0.000013336
## 5515 0001104659-14-015152 voluntarily 1 74985 0.000013336
## 5516 0001104659-14-015152 vtn 1 74985 0.000013336
## 5517 0001104659-14-015152 vulnerable 1 74985 0.000013336
## 5518 0001104659-14-015152 wage 1 74985 0.000013336
## 5519 0001104659-14-015152 waivers 1 74985 0.000013336
## 5520 0001104659-14-015152 wake 1 74985 0.000013336
## 5521 0001104659-14-015152 war 1 74985 0.000013336
## 5522 0001104659-14-015152 warehousing 1 74985 0.000013336
## 5523 0001104659-14-015152 warrant 1 74985 0.000013336
## 5524 0001104659-14-015152 watch 1 74985 0.000013336
## 5525 0001104659-14-015152 wealth 1 74985 0.000013336
## 5526 0001104659-14-015152 week 1 74985 0.000013336
## 5527 0001104659-14-015152 weekly 1 74985 0.000013336
## 5528 0001104659-14-015152 weeks 1 74985 0.000013336
## 5529 0001104659-14-015152 weights 1 74985 0.000013336
## 5530 0001104659-14-015152 welfare 1 74985 0.000013336
## 5531 0001104659-14-015152 west 1 74985 0.000013336
## 5532 0001104659-14-015152 wharf 1 74985 0.000013336
## 5533 0001104659-14-015152 whistle 1 74985 0.000013336
## 5534 0001104659-14-015152 widens 1 74985 0.000013336
## 5535 0001104659-14-015152 window 1 74985 0.000013336
## 5536 0001104659-14-015152 withdrawn 1 74985 0.000013336
## 5537 0001104659-14-015152 withholding 1 74985 0.000013336
## 5538 0001104659-14-015152 words 1 74985 0.000013336
## 5539 0001104659-14-015152 worsening 1 74985 0.000013336
## 5540 0001104659-14-015152 wrong 1 74985 0.000013336
## 5541 0001104659-14-015152 wrongdoing 1 74985 0.000013336
## 5542 0001104659-14-015152 www.sec.gov 1 74985 0.000013336
## 5543 0001104659-14-015152 xviii 1 74985 0.000013336
## # A tibble: 11 x 2
## word value
## <chr> <dbl>
## 1 bastard -5
## 2 ass -4
## 3 abhor -3
## 4 abandon -2
## 5 absentee -1
## 6 some kind 0
## 7 aboard 1
## 8 abilities 2
## 9 admire 3
## 10 amazing 4
## 11 breathtaking 5
## # A tibble: 10 x 2
## word sentiment
## <chr> <chr>
## 1 abandoned anger
## 2 abundance anticipation
## 3 aberration disgust
## 4 abandon fear
## 5 absolution joy
## 6 abandon negative
## 7 abba positive
## 8 abandon sadness
## 9 abandonment surprise
## 10 abacus trust
Loughran & McDonald dictionary – finance specific, targeted at annual reports
## # A tibble: 6 x 2
## word sentiment
## <chr> <chr>
## 1 abide constraining
## 2 abovementioned litigious
## 3 abandon negative
## 4 able positive
## 5 aegis superfluous
## 6 abeyance uncertainty
## Joining, by = "word"
## ID word n total tf sentiment
## 1 0001104659-14-015152 citi 826 74985 0.011015536 <NA>
## 2 0001104659-14-015152 2013 743 74985 0.009908648 <NA>
## 3 0001104659-14-015152 credit 704 74985 0.009388544 <NA>
## 4 0001104659-14-015152 citis 660 74985 0.008801760 <NA>
## 5 0001104659-14-015152 risk 624 74985 0.008321664 uncertainty
## ID word n total tf sentiment
## 5 0001104659-14-015152 risk 624 74985 0.008321664 uncertainty
## 28 0001104659-14-015152 loss 267 74985 0.003560712 negative
## 29 0001104659-14-015152 losses 265 74985 0.003534040 negative
## 36 0001104659-14-015152 approximately 232 74985 0.003093952 uncertainty
## 37 0001104659-14-015152 regulatory 216 74985 0.002880576 litigious
tf_sent %>%
spread(sentiment, tf, fill=0) %>%
select(constraining, litigious, negative, positive, superfluous, uncertainty) %>%
colSums()
## constraining litigious negative positive superfluous uncertainty
## 0.013242649 0.020750817 0.034780289 0.007054744 0.000373408 0.025325065
cast_dfm()
converts tidy term frequencies to Quantedalibrary(knitr)
library(kableExtra)
html_df <- function(text, cols=NULL, col1=FALSE, full=F) {
if(!length(cols)) {
cols=colnames(text)
}
if(!col1) {
kable(text,"html", col.names = cols, align = c("l",rep('c',length(cols)-1))) %>%
kable_styling(bootstrap_options = c("striped","hover"), full_width=full)
} else {
kable(text,"html", col.names = cols, align = c("l",rep('c',length(cols)-1))) %>%
kable_styling(bootstrap_options = c("striped","hover"), full_width=full) %>%
column_spec(1,bold=T)
}
}
cryptoMC <- function(name) {
if (exists(name)) {
get(name)
} else{
html <- getURL(paste('https://coinmarketcap.com/currencies/',name,'/',sep=''))
xpath <- '//*[@id="__next"]/div[1]/div[2]/div[1]/div[2]/div[1]/div/div[2]/span[1]/span[1]/text()'
doc = htmlParse(html, asText=TRUE)
plain.text <- xpathSApply(doc, xpath, xmlValue)
assign(name, gsub("\n","",gsub(" ", "", paste(plain.text, collapse = ""), fixed = TRUE), fixed = TRUE),envir = .GlobalEnv)
get(name)
}
}
# Create a plot of the top words by sentiment
tf_sent %>%
filter(!is.na(sentiment)) %>%
group_by(sentiment) %>%
arrange(desc(n)) %>%
mutate(row = row_number()) %>%
filter(row < 10) %>%
ungroup() %>%
mutate(word = reorder(word, n)) %>%
ggplot(aes(y=n, x=word)) + geom_col() + theme(axis.text.x = element_text(angle=90, hjust=1)) +
facet_wrap(~sentiment, ncol=3, scales="free_x")