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Posts Tagged ‘corpus linguistics’

In two recent papers, one by Kloumann et al. (2012) and the other by Dodds et al. (2015), a group of researchers created a corpus to study the positivity of the English language. I looked at some of the problems with those papers here and here. For this post, however, I want to focus on one of the registers in the authors’ corpus – song lyrics. There is a problem with taking language such as lyrics out of context and then judging them based on the positivity of the words in the songs. But first I need to briefly explain what the authors did.

In the two papers, the authors created a corpus based on books, New York Times articles, tweets and song lyrics. They then created a list of the 10,000 most common word types in their corpus and had voluntary respondents rate how positive or negative they felt the words were. They used this information to claim that human language overall (and English) is emotionally positive.

That’s the idea anyway, but song lyrics exist as part of a multimodal genre. There are lyrics and there is music. These two modalities operate simultaneously to convey a message or feeling. This is important for a couple of reasons. First, the other registers in the corpus do not work like song lyrics. Books and news articles are black text on a white background with few or no pictures. And tweets are not always multimodal – it’s possible to include a short video or picture in a tweet, but it’s not necessary (Side note: I would like to know how many tweets in the corpus included pictures and/or videos, but the authors do not report that information).

So if we were to do a linguistic analysis of an artist or a genre of music, we would create a corpus of the lyrics of that artist or genre. We could then study the topics that are brought up in the lyrics, or even common words and expressions (lexical bundles or n-grams) that are used by the artist(s). We could perhaps even look at how the writing style of the artist(s) changed over time.

But if we wanted to perform an analysis of the positivity of the songs in our corpus, we would need to incorporate the music. The lyrics and music go hand in hand – without the music, you only have poetry. To see what I mean, take a look at the following word list. Do the words in this list look particularly positive or negative to you?

a

ain’t

all

and

as

away

back

bitch

body

breast

but

butterfly

can

can’t

caught

chasing

comin’

days

did

didn’t

do

dog

down

everytime

fairy

fantasy

for

ghost

guess

had

hand

harm

her

his

i

i’m

if

in

it

looked

lovely

jar

makes

mason

life

live

maybe

me

mean

momma’s

more

my

need

nest

never

no

of

on

outside

pet

pin

real

return

robin

scent

she

sighing

slips

smell sorry

that

the

then

think

to

today

told

up

want

wash

went

what

when

with

withered

woke

would

yesterday

you

you’re

your

If we combine these words as Rivers Cuomo did in his song “Butterfly”, they average out to a positive score of 5.23. Here are the lyrics to that song.

Yesterday I went outside
With my momma’s mason jar
Caught a lovely Butterfly
When I woke up today
And looked in on my fairy pet
She had withered all away
No more sighing in her breast

I’m sorry for what I did
I did what my body told me to
I didn’t mean to do you harm
But everytime I pin down what I think I want
it slips away – the ghost slips away

I smell you on my hand for days
I can’t wash away your scent
If I’m a dog then you’re a bitch
I guess you’re as real as me
Maybe I can live with that
Maybe I need fantasy
A life of chasing Butterfly

I’m sorry for what I did
I did what my body told me to
I didn’t mean to do you harm
But everytime I pin down what I think I want
it slips away – the ghost slips away

I told you I would return
When the robin makes his nest
But I ain’t never comin’ back
I’m sorry, I’m sorry, I’m sorry

Does this look like a positive text to you? Does it look moderate, neither positive nor negative? I would say not. It seems negative to me, a sad song based on the opera Madame Butterfly, in which a man leaves his wife because he never really cared for her. When we include the music into our consideration, the non-positivity of this song is clear.


Let’s take a look at another list. How does this one look?

above

absence

alive

an

animal

apart

are

away

become

brings

broke

can

closer

complicate

desecrate

down

drink

else

every

everything

existence

faith

feel

flawed

for

forest

from

fuck

get

god

got

hate

have

help

hive

honey

i

i’ve

inside

insides

is

isolation

it

it’s

knees

let

like

make

me

my

myself

no

of

off

only

penetrate

perfect

reason

scraped

sell

sex

smell

somebody

soul

stay

stomach

tear

that

the

thing

through

to

trees

violate

want

whole

within

works

you

your

Based on the ratings in the two papers, this list is slightly more positive, with an average happiness rating of 5.46. When the words were used by Trent Reznor, however, they expressed “a deeply personal meditation on self-hatred” (Huxley 1997: 179). Here are the lyrics for “Closer” by Nine Inch Nails:

You let me violate you
You let me desecrate you
You let me penetrate you
You let me complicate you

Help me
I broke apart my insides
Help me
I’ve got no soul to sell
Help me
The only thing that works for me
Help me get away from myself

I want to fuck you like an animal
I want to feel you from the inside
I want to fuck you like an animal
My whole existence is flawed
You get me closer to god

You can have my isolation
You can have the hate that it brings
You can have my absence of faith
You can have my everything

Help me
Tear down my reason
Help me
It’s your sex I can smell
Help me
You make me perfect
Help me become somebody else

I want to fuck you like an animal
I want to feel you from the inside
I want to fuck you like an animal
My whole existence is flawed
You get me closer to god

Through every forest above the trees
Within my stomach scraped off my knees
I drink the honey inside your hive
You are the reason I stay alive

As Reznor (the songwriter and lyricist) sees it, “Closer” is “supernegative and superhateful” and that the song’s message is “I am a piece of shit and I am declaring that” (Huxley 1997: 179). You can see what he means when you listen to the song (minor NSF warning for the imagery in the video). [1]

Nine Inch Nails: Closer (Uncensored) (1994) from Nine Inch Nails on Vimeo.

Then again, meaning is relative. Tommy Lee has said that “Closer” is “the all-time fuck song. Those are pure fuck beats – Trent Reznor knew what he was doing. You can fuck to it, you can dance to it and you can break shit to it.” And Tommy Lee should know. He played in the studio for NIИ and he is arguably more famous for fucking than he is for playing drums.

Nevertheless, the problem with the positivity rating of songs keeps popping up. The song “Mad World” was a pop hit for Tears for Fears, then reinterpreted in a more somber tone by Gary Jules and Michael Andrews. But it is rated a positive 5.39. Gotye’s global hit about failed relationships, “Somebody That I Used To Know”, is rated a positive 5.33. The anti-war and protest ballad “Eve of Destruction”, made famous by Barry McGuire, rates just barely on the negative side at 4.93. I guess there should have been more depressing references besides bodies floating, funeral processions, and race riots if the song writer really wanted to drive home the point.

For the song “Milkshake”, Kelis has said that it “means whatever people want it to” and that the milkshake referred to in the song is “the thing that makes women special […] what gives us our confidence and what makes us exciting”. It is rated less positive than “Mad World” at 5.24. That makes me want to doubt the authors’ commitment to Sparkle Motion.

Another upbeat jam that the kids listen to is the Ramones’ “Blitzkrieg Bop”. This is the energetic and exciting anthem of punk rock. It’s rated a negative 4.82. I wonder if we should even look at “Pinhead”.

Then there’s the old American folk classic “Where did you sleep last night”, which Nirvana performed a haunting version of on their album MTV Unplugged in New York. The song (also known as “In the Pines” and “Black Girl”) was first made famous by Lead Belly and it includes such catchy lines as

My girl, my girl, don’t lie to me
Tell me where did you sleep last night
In the pines, in the pines
Where the sun don’t ever shine
I would shiver the whole night through

And

Her husband was a hard working man
Just about a mile from here
His head was found in a driving wheel
But his body never was found

This song is rated a positive 5.24. I don’t know about you but neither the Lead Belly version, nor the Nirvana cover would give me that impression.

Even Pharrell Williams’ hit song “Happy” rates only 5.70. That’s a song so goddamn positive that it’s called “Happy”. But it’s only 0.03 points more positive than Eric Clapton’s “Tears in Heaven”, which is a song about the death of Clapton’s four-year-old son. Harry Chapin’s “Cat’s in the Cradle” was voted the fourth saddest song of all time by readers of Rolling Stone but it’s rated 5.55, while Willie Nelson’s “Always on My Mind” rates 5.63. So they are both sadder than “Happy”, but not by much. How many lyrics must a man research, before his corpus is questioned?

Corpus linguistics is not just gathering a bunch of words and calling it a day. The fact that the same “word” can have several meanings (known as polysemy), is a major feature of language. So before you ask people to rate a word’s positivity, you will want to make sure they at least know which meaning is being referred to. On top of that, words do not work in isolation. Spacing is an arbitrary construct in written language (remember that song lyrics are mostly heard not read). The back used in the Ramones’ lines “Piling in the back seat” and “Pulsating to the back beat” are not about a body part. The Weezer song “Butterfly” uses the word mason, but it’s part of the compound noun mason jar, not a reference to a brick layer. Words are also conditioned by the words around them. A word like eve may normally be considered positive as it brings to mind Christmas Eve and New Year’s Eve, but when used in a phrase like “the eve of destruction” our judgment of it is likely to change. In the corpus under discussion here, eat is rated 7.04, but that doesn’t consider what’s being eaten and so can not account for lines like “Eat your next door neighbor” (from “Eve of Destruction”).

We could go on and on like this. The point is that the authors of both of the papers didn’t do enough work with their data before drawing conclusions. And they didn’t consider that some of the language in their corpus is part of a multimodal genre where there are other things affecting the meaning of the language used (though technically no language use is devoid of context). Whether or not the lyrics of a song are “positive” or “negative”, the style of singing and the music that they are sung to will highly effect a person’s interpretation of the lyrics’ meaning and emotion. That’s just the way that music works.

This doesn’t mean that any of these songs are positive or negative based on their rating, it means that the system used by the authors of the two papers to rate the positivity or negativity of language seems to be flawed. I would have guessed that a rating system which took words out of context would be fundamentally flawed, but viewing the ratings of the songs in this post is a good way to visualize that. The fact that the two papers were published in reputable journals and picked up by reputable publications, such as the Atlantic and the New York Times, only adds insult to injury for the field of linguistics.

You can see a table of the songs I looked at for this post below and an spreadsheet with the ratings of the lyrics is here. I calculated the positivity ratings by averaging the scores for the word tokens in each song, rather than the types.

(By the way, Tupac is rated 4.76. It’s a good thing his attitude was fuck it ‘cause motherfuckers love it.)

Song Positivity score (1–9)
“Happy” by Pharrell Williams 5.70
“Tears in Heaven” by Eric Clapton 5.67
“You Were Always on My Mind” by Willie Nelson 5.63
“Cat’s in the Cradle” by Harry Chapin 5.55
“Closer” by NIN 5.46
“Mad World” by Gary Jules and Michael Andrews 5.39
“Somebody that I Used to Know” by Gotye feat. Kimbra 5.33
“Waitin’ for a Superman” by The Flaming Lips 5.28
“Milkshake” by Kelis 5.24
“Where Did You Sleep Last Night” by Nirvana 5.24
“Butterfly” by Weezer 5.23
“Eve of Destruction” by Barry McGuire 4.93
“Blitzkrieg Bop” by The Ramones 4.82

 

Footnotes

[1] Also, be aware that listening to these songs while watching their music videos has an effect on the way you interpret them. (Click here to go back up.)

References

Isabel M. Kloumann, Christopher M. Danforth, Kameron Decker Harris, Catherine A. Bliss, Peter Sheridan Dodds. 2012. “Positivity of the English Language”. PLoS ONE. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0029484

Dodds, Peter Sheridan, Eric M. Clark, Suma Desu, Morgan R. Frank, Andrew J. Reagan, Jake Ryland Williams, Lewis Mitchell, Kameron Decker Harris, Isabel M. Kloumann, James P. Bagrow, Karine Megerdoomian, Matthew T. McMahon, Brian F. Tivnan, and Christopher M. Danforth. 2015. “Human language reveals a universal positivity bias”. PNAS 112:8. http://www.pnas.org/content/112/8/2389

Huxley, Martin. 1997. Nine Inch Nails. New York: St. Martin’s Griffin.

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Last week I wrote a post called “If you’re not a linguist, don’t do linguistics”. This got shared around Twitter quite a bit and made it to the front page of r/linguistics, so a lot of people saw it. Pretty much everyone had good insight on the topic and it generated some great discussion. I thought it would be good to write a follow-up to flesh out my main concerns in a more serious manner (this time sans emoticons!) and to address the concerns some people had with my reasoning.

The paper in question is by Dodds et al. (2015) and it is called “Human language reveals a universal positivity bias”. The certainty of that title is important since I’m going to try to show in this post that the authors make too many assumptions to reliably make any claims about all human language. I’m going to focus on the English data because that is what I am familiar with. But if anyone who is familiar with the data in other languages would like to weigh in, please do so in the comments.

The first assumption made by the authors is that it is possible to make universal claims about language using only written data. This is not a minor issue. The differences between spoken and written language are many and major (Linell 2005). But dealing with spoken data is difficult – it takes much more time and effort to collect and analyze than written data. We can argue, however, that even in highly literate societies, the majority of language use is spoken – and spoken language does not work like written language. This is an assumption that no scholar should ever make. So any research which makes claims about all human language will therefore have to include some form of spoken data. But the data set that the authors draw from (called their corpus) is made from tweets, song lyrics, New York Times articles and the Google Books project. Tweets and song lyrics, let alone news articles or books, do not mimic spoken language in an accurate way. For example, these registers may include the same words as human speech, but certainly not in the same proportion. Written language does not include false starts, nor does it include repetition or elusion in near the same way that spoken language does. Anyone who has done any transcription work will tell you this.

The next assumption made by the authors is that their data is representative of all human language. Representativeness is a major issue in corpus linguistics. When linguists want to investigate a register or variety of language, they build a corpus which is representative of that register or variety by taking a large enough and balanced sample of texts from that register. What is important here, however, is that most linguists do not have a problem with a set of data representing a larger register – so long as that larger register isn’t all human language. For example, if we wanted to research modern English journalism (quite a large register), we would build a corpus of journalism texts from English-speaking countries and we would be careful to include various kinds of journalism – op-eds, sports reporting, financial news, etc. We would not build a corpus of articles from the Podunk Free Press and make claims about all English journalism. But representativeness is a tricky issue. The larger the language variety you are trying to investigate, the more data from that variety you will need in your corpus. Baker (2010: 7) notes that a corpus analysis of one novel is “unlikely to be representative of all language use, or all novels, or even the general writing style of that author”. The English sub-corpora in Dodds et al. exists somewhere in between a fully non-representative corpus of English (one novel) and a fully representative corpus of English (all human speech and writing in English). In fact, in another paper (Dodds et al. 2011), the representativeness of the Twitter corpus is explained as “First, in terms of basic sampling, tweets allocated to data feeds by Twitter were effectively chosen at random from all tweets. Our observation of this apparent absence of bias in no way dismisses the far stronger issue that the full collection of tweets is a non-uniform subsampling of all utterances made by a non-representative subpopulation of all people. While the demographic profile of individual Twitter users does not match that of, say, the United States, where the majority of users currently reside, our interest is in finding suggestions of universal patterns.”. What I think that doozy of a sentence in the middle is saying is that the tweets come from an unrepresentative sample of the population but that the language in them may be suggestive of universal English usage. Does that mean can we assume that the English sub-corpora (specifically the Twitter data) in Dodds et al. is representative of all human communication in English?

Another assumption the authors make is that they have sampled their data correctly. The decisions on what texts will be sampled, as Tognini-Bonelli (2001: 59) points out, “will have a direct effect on the insights yielded by the corpus”. Following Biber (see Tognini-Bonelli 2001: 59), linguists can classify texts into various channels in order to assure that their sample texts will be representative of a certain population of people and/or variety of language. They can start with general “channels” of the language (written texts, spoken data, scripted data, electronic communication) and move on to whether the language is private or published. Linguists can then sample language based on what type of person created it (their age, sex, gender, social-economic situation, etc.). For example, if we made a corpus of the English articles on Wikipedia, we would have a massive amount of linguistic data. Literally billions of words. But 87% of it will have been written by men and 59% of it will have been written by people under the age of 40. Would you feel comfortable making claims about all human language based on that data? How about just all English language encyclopedias?

The next assumption made by the authors is that the relative positive or negative nature of the words in a text are indicative of how positive that text is. But words can have various and sometimes even opposing meanings. Texts are also likely to contain words that are written the same but have different meanings. For example, the word fine in the Dodds et al. corpus, like the rest of the words in the corpus, is just a four letter word – free of context and naked as a jaybird. Is it an adjective that means “good, acceptable, or satisfactory”, which Merriam-Webster says is sometimes “used in an ironic way to refer to things that are not good or acceptable”? Or does it refer to that little piece of paper that the Philadelphia Parking Authority is so (in)famous for? We don’t know. All we know is that it has been rated 6.74 on the positivity scale by the respondents in Dodds et al. Can we assume that all the uses of fine in the New York Times are that positive? Can we assume that the use of fine on Twitter is always or even mostly non-ironic? On top of that, some of the most common words in English also tend to have the most meanings. There are 15 entries for get in the Macmillan Dictionary, including “kill/attack/punish” and “annoy”. Get in Dodds et al. is ranked on the positive side of things at 5.92. Can we assume that this rating carries across all the uses of get in the corpus? The authors found approximately 230 million unique “words” in their Twitter corpus (they counted all forms of a word separately, so banana, bananas, b-a-n-a-n-a-s! would be separate “words”; and they counted URLs as words). So they used the 50,000 most frequent ones to estimate the information content of texts. Can we assume that it is possible to make an accurate claim about how positive or negative a text is based on nothing but the words taken out of context?

Another assumption that the authors make is that the respondents in their survey can speak for the entire population. The authors used Amazon’s Mechanical Turk to crowdsource evaluations for the words in their sub-corpus. 60% of the American people on Mechanical Turk are women and 83.5% of them are white. The authors used respondents located in the United States and India. Can we assume that these respondents have opinions about the words in the corpus that are representative of the entire population of English speakers? Here are the ratings for the various ways of writing laughter in the authors’ corpus:

Laughter tokens Rating
ha 6
hah 5.92
haha 7.64
hahah 7.3
hahaha 7.94
hahahah 7.24
hahahaha 7.86
hahahahaha 7.7
ha 6
hee 5.4
heh 5.98
hehe 6.48
hehehe 7.06

And here is a picture of a character expressing laughter:

Pictured: Good times. Credit: Batman #36, DC Comics, Scott Snyder (wr), Greg Capullo (p), Danny Miki (i), Fco Plascenia (c), Steve Wands (l).

Pictured: Good times. Credit: Batman #36, DC Comics, Scott Snyder (wr), Greg Capullo (p), Danny Miki (i), Fco Plascenia (c), Steve Wands (l).

Can we assume that the textual representation of laughter is always as positive as the respondents rated it? Can we assume that everyone or most people on Twitter use the various textual representations of laughter in a positive way – that they are laughing with someone and not at someone?
Finally, let’s compare some data. The good people at the Corpus of Contemporary American English (COCA) have created a word list based on their 450 million word corpus. The COCA corpus is specifically designed to be large and balanced (although the problem of dealing with spoken language might still remain). In addition, each word in their corpus is annotated for its part of speech, so they can recognize when a word like state is either a verb or a noun. This last point is something that Dodds et al. did not do – all forms of words that are spelled the same are collapsed into being one word. The compilers of the COCA list note that “there are more than 140 words that occur both as a noun and as a verb at least 10,000 times in COCA”. This is the type/token issue that came up in my previous post. A corpus that tags each word for its part of speech can tell the difference between different types of the “same” word (state as a verb vs. state as a noun), while an untagged corpus treats all occurrences of state as the same token. If we compare the 10,000 most common words in Dodds et al. to a sample of the 10,000 most common words in COCA, we see that there are 121 words on the COCA list but not the Dodds et al. list (Here is the spreadsheet from the Dodds et al. paper with the COCA data – pnas.1411678112.sd01 – Dodds et al corpus with COCA). And that’s just a sample of the COCA list. How many more differences would there be if we compared the Dodds et al. list to the whole COCA list?

To sum up, the authors use their corpus of tweets, New York Times articles, song lyrics and books and ask us to assume (1) that they can make universal claims about language despite using only written data; (2) that their data is representative of all human language despite including only four registers; (3) that they have sampled their data correctly despite not knowing what types of people created the linguistic data and only including certain channels of published language; (4) that the relative positive or negative nature of the words in a text are indicative of how positive that text is despite the obvious fact that words can be spelled the same and still have wildly different meanings; (5) that the respondents in their survey can speak for the entire population despite the English-speaking respondents being from only two subsets of two English-speaking populations (USA and India); and (6) that their list of the 10,000 most common words in their corpus (which they used to rate all human language) is representative despite being uncomfortably dissimilar to a well-balanced list that can differentiate between different types of words.

I don’t mean to sound like a Negative Nancy and I don’t want to trivialize the work of the authors in this paper. The corpus that they have built is nothing short of amazing. The amount of feedback they got from human respondents on language is also impressive (to say the least). I am merely trying to point out what we can and can not say based on the data. It would be nice to make universal claims about all human language, but the fact is that even with millions and billions of data points, we still are not able to do so unless the data is representative and sampled correctly. That means it has to include spoken data (preferably a lot of it) and it has to be sampled from all socio-economic human backgrounds.

Hat tip to the commenters on the last post and the redditors over at r/linguistics.

References

Dodds, Peter Sheridan, Eric M. Clark, Suma Desu, Morgan R. Frank, Andrew J. Reagan, Jake Ryland Williams, Lewis Mitchell, Kameron Decker Harris, Isabel M. Kloumann, James P. Bagrow, Karine Megerdoomian, Matthew T. McMahon, Brian F. Tivnan, and Christopher M. Danforth. 2015. “Human language reveals a universal positivity bias”. PNAS 112:8. http://www.pnas.org/content/112/8/2389

Dodds, Peter Sheridan, Kameron Decker Harris, Isabel M. Koumann, Catherine A. Bliss, Christopher M. Danforth. 2011. “Temporal Patterns of Happiness and Information in a Global Social Network: Hedonometrics and Twitter”. PLOS One. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0026752#abstract0

Baker, Paul. 2010. Sociolinguistics and Corpus Linguistics. Edinburgh: Edinburgh University Press. http://www.ling.lancs.ac.uk/staff/paulb/socioling.htm

Linell, Per. 2005. The Written Language Bias in Linguistics. Oxon: Routledge.

Mair, Christian. 2015. “Responses to Davies and Fuchs”. English World-Wide 36:1, 29–33. doi: 10.1075/eww.36.1.02mai

Tognini-Bonelli, Elena. 2001. Studies in Corpus Linguistics, Volume 6: Corpus Linguistics as Work. John Benjamins. https://benjamins.com/#catalog/books/scl.6/main

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When Google’s Ngram Viewer was the topic of a post on Science-Based Medice, I knew it was becoming mainstream. No longer happy to only be toyed with by linguists killing time, the Ngram Viewer had entranced people from other walks of life. And I can understand why. Google’s Ngram Viewer is an impressive service that allows you to quickly and easily search for the frequency of words and phrases in millions of books. But I want to warn you about Google’s Ngram Viewer. As a corpus linguist, I think it’s important to explain just what Ngram Viewer is, what it can be used to do, how I feel about it, and the praise it has been receiving since its inception. I’ll start out simple: despite all its power and what it seems to be capable of, looks can be deceiving.

Have we learned nothing?

Jann Bellamy wrote a post at Science-Based Medicine about using Google’s Ngram Viewer (GNV) to research some terms used to describe the very unscientific practice of Complementary and Alternative Medicine (CAM). Although an article of this type is unusual for the SBM site, it does show how intriguing GNV can be. And Ms. Bellamy does a good job by explaining a few of the caveats of GVN:

The database only goes through 2008, so searches have to end there. Also, the searches have to assume that the word or phrase has only one definition, or perhaps one definition that dominates all others. We also have to remember that only books were scanned, not, for example, academic journals or popular magazines. Or blog posts, for that matter.

Ms. Bellamy then goes on the search for some CAM terms. After noting the which terms are more common and when they started to rise in usage, she does a very good job at explaining the reasons that certain terms have a higher frequency than others. At the end, however, she is left with more questions than answers. Although she discovered that alternative medicine appears more frequently than complementary medicine in the Google Books database, and although she did further research (outside of Google Books) to explain why, she is still left right where she started. Just looking at the numbers from GNV, she can’t say what kind of impact CAM has had on our (English-speaking) world or culture. So what was the point of looking as GNV at all (besides the pretty colors)?

In her post, Ms. Bellamy links to an article in the New York Times by Natasha Singer. In what is essentially a exposition of GNV, with quotes from two of its founders, Ms. Singer places a lot more stock in the value and capability of the program. But from a corpus linguist’s perspective, she leaps a bit too far to her conclusions.

Ms. Singer’s article begins with the phrase “Data is the new oil” and then goes on to explain the comparison between these two words offered by GNV. She writes:

I started my data-versus-oil quest with casual one-gram queries about the two words. The tool produced a chart showing that the word “data” appeared more often than “oil” in English-language texts as far back as 1953, and that its frequency followed a steep upward trajectory into the late 1980s. Of course, in the world of actual commerce, oil may have greater value than raw data. But in terms of book mentions, at least, the word-use graph suggests that data isn’t simply the new oil. It’s more like a decades-old front-runner.

But with the Google Books corpus (the set of texts that GNV analyzes), we need to remember what the corpus contains, i.e. what “book mentions” means. This lets us know how representative both the corpus and our analysis is. The Google Books corpus does not contain speech, newspapers, tweets, magazine articles, business letters, or financial reports. Sure, oil is important to our culture, and certainly to global and political history, but do people write books about it? We can not directly extrapolate the findings from Google Books to Culture any more than we can tell people about the world of 16th Century England by studying the plays of Shakespeare. With GNV we can merely study the culture of books (or the culture of publishing). And there are many ways that GNV can mislead you. For example, are the hits in Ms. Singer’s search talking about crude oil, olive oil, or oil paintings? Google Ngrams will not tell you. Just for fun, here’s Ms. Singer’s search redone with some other terms. Feel free to draw your own conclusions.

Click to embiggen

Search for “data, oil, chocolate, love” on GNV. (Just to be clear, searching for oil_NOUN doesn’t change things much; oil as a verb is almost non-existent in the corpus. Take that as you will)

Research casual

The second article I want to talk about comes from Ben Zimmer. While I don’t think Mr. Zimmer needs to be told anything that’s in this post, his article in The Atlantic gets to the heart of my frustration with GNV. It features a more complex search on GNV to find out which nouns modify the word mogul and how they have changed over the last 100 years. In the following passage, he alludes to the reality of GNV without coming right out and saying it.

It’s possible to answer these questions using the publicly available corpora compiled by Mark Davies at Brigham Young University, but the peculiar interface can be off-putting to casual users. With the Ngram Viewer, you just need to enter a search like “*_NOUN mogul” or “ragtag *_NOUN” and select a year range. It turns out that in 20th-century sources, media moguls are joined by movie moguls, real estate moguls, and Hollywood moguls, while the most likely things to be ragtag are armies, groups, and bands.

There are a few points to make about this. First, the interface of the publicly available corpora compiled by Mark Davies could be described as “peculiar”, but that’s only because it’s not the lowest common denominator. And there’s the rub because researchers are capable of so much more using Mark Davies’ corpora. While the interface isn’t immediately intuitive, it certainly isn’t hard to learn. As a bad comparison, think about the differences between Windows, OSX, and a Linux OS. Windows is the lowest common denominator – easiest to use and most intuitive. OSX and Linux, on the other hand, take a bit of getting used to. But how many of us have learned OSX or Linux and willingly gone back to Windows?

The second point is not so much about casual users as it is about casual searches. I think Mr. Zimmer is right to talk about casual users since it’s probable that most of the people who use GNV will be looking for a quick and easy stroll down the cultural garden path. But more to the point, I think he’s right to offer different types of moguls as a search example because that’s about as far as GNV will take you. Can you see which types of moguls people are talking about? No. How about which types of moguls are being used in magazines? Nope. Newspapers? Nuh-uh. You have to turn to one of Mark Davies’ corpora for that. In fact, less casual users are even able to access Google Books (and other corpora) via Mark Davies’ site, and this allows them to conduct more complex searches (For a much more detailed comparison of GNV and some of the corpora offered on Mark Davies’ site, see here). So again the question is what’s the point of looking at GNV at all?

Final thoughts – Almost right but not quite

All this picking on GNV is not without reason. Even though what the people at Google have done is truly impressive, we have seen that the practical use GNV is limited. As the saying in corpus linguistics goes “Compiling is only half the battle”. GNV does not offer users a way to really measure what they are (usually) looking for. As an example, a quote from Ms. Singer’s article will suffice:

The system can also conduct quantitative checks on popular perceptions. Consider our current notion that we live in a time when technology is evolving faster than ever. Mr. Aiden and Mr. Michel [two of GNV’s creators] tested this belief by comparing the dates of invention of 147 technologies with the rates at which those innovations spread through English texts. They found that early 19th-century inventions, for instance, took 65 years to begin making a cultural impact, while turn-of-the-20th-century innovations took only 26 years. Their conclusion: the time it takes for society to learn about an invention has been shrinking by about 2.5 years every decade.

While this may be true, it’s not proven by looking at Google Books. For example, ask yourself these questions: what was the rate of literacy in the early 19th-century? How many books did people read (or have read to them) in the early 19th-century compared to the turn of the 20th century? What was the difference between the rate of dissemination of information in the two time periods? How about the rate of publishing? And what exactly qualifies as technology – farm equipment or fMRI machines? Or does it have to be more closely related to culture and Culturomics – like Facebook?

And most importantly, are books the best way to measure the cultural impact of an idea or technology? The fact is that the system can not really conduct quantitative checks on popular perceptions. But it can make you think it can.

So GNV has a long way to go. I hesitate to say that they will get there because Google does not really have an interest in offering this kind of service to the public (I didn’t see any ads on the GNV page, did you?). While it may be fun to play around with GNV, I would advise against drawing any (serious) conclusions from what it spits out. Below are some other searches I ran. Again, feel free to draw your own conclusions about how these terms and the things they describe relate to human culture.

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Search for “blood, sugar, sex, magik”. Click here to see the results on GNV.

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Search for “Booby Fischer, Jay Z”. Click here to see the results on GNV.

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Search for “Bobby Fischer, Jay Z, Eminem, Dr Dre, Run DMC, Noam Chomsky”. Click here to see the results on GNV.

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Search for “Johnny Carson, Conan O’Brien, Jay Leno, David Letterman, Jimmy Kimmel, Jimmy Fallon, Big Bird, Saturday Night Live” (from the year 1950). Click here and then “Search lots of books” to see the results on GNV.

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Search for “Superman, Batman, Wonder Woman, Buffy, King Arthur, Robin Hood, Hercules, Sherlock Holmes, Pele”. Click here to see the results on GNV.

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Search for “Barack Obama, George Bush, Bill Clinton, Ronald Reagan, Richard Nixon, John * Kennedy, Dwight * Eisenhower, Harry * Truman, Franklin * Roosevelt, Abraham Lincoln, Beatles”. Click here to see the results on GNV.


Notice how the middle initial of some presidents complicates things in the above search. It would be nice to be able to combine the frequencies for “John Fitzgerald Kennedy”, “John F Kennedy”, “John Kennedy”, and “JFK” into one line, and exclude hits like “John S Kennedy” from the results completely, but that’s not possible. You could, however, search GNV for the different ways to refer to President Kennedy and see the differences, for whatever that will tell you.
 
 
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Search for “Brad Pitt, Audrey Hepburn, Noam Chomsky, Bob Marley”. Click here to see the result on GNV.


Noam Chomsky has had a bigger effect on our culture than Audrey Hepburn, Bob Marley, and Brad Pitt? You be the judge!

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This post is a response to a corpus search done on another blog. Over on What You’re Doing Is Rather Desperate, Neil Saunders wanted to research how adverbs are used in academic articles, specifically the sentence adverb, or as he says, adverbs which are used “with a comma to make a point at the start of a sentence”. I’m not trying to pick on Mr. Saunders (because what he did was pretty great for a non-linguist), but I think his post, and the media reports on it, makes a great excuse to write about the really, really awesome corpus linguistics resources available to the public. I’ll go through what Mr. Saunders did, and list what he could have done had he known about corpus linguistics.

Mr. Saunders wanted to know about sentence adverbs in academic texts so he wrote a script to download abstracts from PubMed Central. Right off the bat, he could have gone looking for either (1) articles on sentence adverbs or (2) already available corpora. As I pointed out in a comment on his post (which has mysteriously disappeared, probably due to the URLs I in it), there are corpora with science texts from as far back as the 1375 AD. There are also modern alternatives, such as the Corpus of Contemporary American English (COCA) and the British National Corpus (BNC), both of which (and much, much more) are available through Mark Davies’ awesome site.

I bring this up because there are several benefits of using these corpora instead of compiling your own, especially if you’re not a linguist. The first is time and space. Saunders says that his uncompressed corpus of abstracts is 47 GB (!) and that it took “overnight” (double !) for his script to comb through the abstracts. Using an online corpus drops the space required on your home machine down to 0 GB. And running searches on COCA, which contains 450 million words, takes a matter of seconds.

The second benefit is a pretty major one for linguists. After noting that his search only looks for words ending in -ly, Saunders says:

There will of course be false positives – words ending with “ly,” that are not adverbs. Some of these include: the month of July, the country of Italy, surnames such as Whitely, medical conditions such as renomegaly and typographical errors such as “Findingsinitially“. These examples are uncommon and I just ignore them where they occur.

This is a big deal. First of all, the idea of using “ly” as a way to search for adverbs is profoundly misguided. Saunders seems to realize this, since he notes that not all words that end in -ly are adverbs. But where he really goes wrong, as we’ll soon see, is in disregarding all of the adverbs that do not end in -ly. If Saunders had used a corpus that already had each word tagged for its part of speech (POS), or if he had ran a POS-tagger on his own corpus, he could have had an accurate measurement of the use of adverbs in academic articles. This is because POS-tagging allows researchers to find adverbs, adjectives, nouns, etc., as well as searching for words that end in -ly – or even just adverbs that end in -ly. And remember, it can all be done in a matter of moments (even the POS tagging). You won’t even have time to make a cup of coffee, although consumption of caffeinated beverages is highly recommended when doing linguistics (unless you’re at a conference, in which case you should substitute alcohol for caffeine).

Here is where I break from following Saunders’ method. I want like to show you what’s possible with some of the publicly available corpora online, or how a linguist would conduct an inquiry into the use of adverbs in academia.

Looking for sentence-initial adverbs in academic texts, I went to COCA. I know the COCA interface can seem a bit daunting to the uninitiated, but there are very clear instructions (with examples) of how to do everything. Just remember: if confusion persists for more than four hours, consult your local linguist.

On the COCA page, I searched for adverbs coming after a period, or sentence initial adverbs, in the Medical and Science/Technology texts in the Academic section (Click here to rerun my exact search on COCA. Just hit “Search” on the left when you get there). Here’s what I came up with:

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Top ten sentence initial adverbs in medical and science academic texts in COCA.

You’ll notice that only one of the adverbs on this list (“finally”) ends in “ly”. That word is also coincidentally the top word on Saunders’ list. Notice also that the list above includes the kind of sentence adverbs that Saunders’ search deliberately does not, or those not ending in -ly, such as “for” and “in”, despite the examples of such given on the Wikipedia page that Saunders linked to in his post. (For those wondering, the POS-tagger treated these as parts of adverbial phrases, hence the “REX21” and “RR21” tags)

Searching for only those sentence initial adverbs that end in -ly, we find a list similar to Saunders’, but with only five of the same words on it. (Saunders’ top ten are: finally, additionally, interestingly, recently, importantly, similarly, surprisingly, specifically, conversely, consequentially)

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Top ten sentence initial adverbs ending in -ly in medical and science academic texts in COCA.

So what does this tell us? Well, for starters, my shooting-from-the-hip research is insufficient to draw any great conclusions from, even if it is more systematic than Saunders’. Seeing what adverbs are used to start sentences doesn’t really tell us much about, for example, what the journals, authors, or results of the papers are like. This is the mistake that Mr. Saunders makes in his conclusions. After ranking the usage frequencies of surprising by journal, he writes:

The message seems clear: go with a Nature or specialist PLoS journal if your results are surprising.

Unfortunately for Mr. Saunders, a linguist would find the message anything but clear. For starters, the realtive use of surprising in a journal does not tell us that the results in the articles are actually surprising, but rather that the authors wish to present their results as surprising. That is, if the word surprising in the articles is not preceded by Our results are not. This is another problem with Mr. Saunders’ conclusions – not placing his results in context – and it is something that linguists would research, perhaps by scrolling through the concordances using corpus linguistics software, or software designed exactly for the type of research that Mr. Saunders wished to do.

The second thing to notice about my results is that they probably look a whole lot more boring than Saunders’. Such is the nature of researching things that people think matter (like those nasty little adverbs), but professionals know really don’t. So it goes.

Finally, what we really should be looking at is how scientists use adverbs in comparison to other writers. I chose to contrast the frequencies of sentence-initial adverbs in the medical and science/technology articles with the frequencies found in academic articles from the (oft-disparaged) humanities. (Here is the link to that search.)

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Top ten sentence initial adverbs in humanities academic texts in COCA.

Six of the top ten sentence initial adverbs in the humanities texts are also on the list for the (hard) science texts. What does this tell us? Again, not much. But we can get an idea that either the styles in the two subjects are not that different, or that sentence initial adverbs might be similar across other genres as well (since the words on these lists look rather pedestrian). We won’t know, of course, until we do more research. And if you really want to know, I suggest you do some corpus searches of your own because the end of this blog post is long overdue.

I also think I’ve picked on Mr. Saunders enough. After all, it’s not really his fault if he didn’t do as I have suggested. How was he supposed to know all these corpora are available? He’s a bioinformatician, not a corpus linguist. And yet, sadly, he’s the one who gets written up in the Smithsonian’s blog, even though linguists have been publishing about these matters since at least the late 1980s.

Before I end, though, I want to offer a word of warning. Although I said that anyone who knows where to look can and should do their own corpus linguistic research, and although I tried to keep my searches as simple as possible, I couldn’t have done them without my background in linguistics. Doing linguistic research on Big Data is tempting. But doing linguistic research on a corpora, especially one that you compiled, can be misleading at best and flat out wrong at worst if you don’t know what you’re doing. The problem is that Mr. Saunders isn’t alone. I’ve seen other non-linguists try this type of research. My message here is similar to the one in my previous post, which was directed to marketers: linguistic research is interesting and it can tell you a lot about the subject of your interest, but only if you do it right. So get a linguist to do it or see if a linguist has already done it. If either of these is not possible, then feel free to do your own research, but tread lightly, young padawans.

If you’re wondering whether academia overuses adverbs (hint: it doesn’t) or just how much adverbs get tossed into academic articles, I recommend reading papers written by Douglas Biber and/or Susan Conrad. They have published extensively on the linguistic nature of many different writing genres. Here’s a link to a Google Scholar search to get you started. You can also have a look at the Longman Grammar, which is probably available at your library.

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This post is intended to show what linguistics can offer marketing. I’ll be using corpus linguistics tools to analyze a few pieces of advice about how to write better marketing copy. The idea is to empirically test the ideas of what makes for more profitable marketing. But first, a quick note to the marketers. Linguists, please leave the room.

Note to marketers: Corpus linguistics works by annotating texts according to the linguistic features that one wishes to study. One of the most common ways is to tag each word for its part of speech (noun, verb, etc.) and that is what I’ve done here. Corpus linguistics generally works better on longer texts or larger banks of texts, since the results of the analysis become more accurate with more data. In this post I’m going to do a surface analysis of email marketing texts, which are each 250-300 words long, using corpus linguistic methods. If you’re interested in knowing more, please feel free to contact me (joseph.mcveigh@gmail.com). In fact, I really hope you’ll get in touch because I’ve tried again and again to get email marketers to work with me and come up with bupkis. I’m writing this post to show you exactly what I have to offer, which is something you won’t find anywhere else.

Welcome back, linguists. So what I’ve done is gathered ten email marketing texts and ranked them based on how well they performed. That means I divided the number of units sold by the number of emails sent. I then ran each text through a part-of-speech tagger (CLAWS7). Now we’re ready for action.

Let’s start with a few pieces of advice about how to write good marketing copy. I want to see if the successful and unsuccessful marketing texts show whether the advice really translates into better sales.

1. Don’t BE yourself

The first piece of advice goes like this: Don’t use BE verbs in your writing. This means copywriters should avoid is, are, was, were, etc. because it apparently promotes insanity (test results pending) and because “we never can reduce ourselves to single concepts”. If it sounds crazy, that’s because it is. And even the people who promote this advice can’t follow it (three guesses as to what the fourth word in the section on that page introducing this advice is). But let’s see what the marketing texts tell us. Who knows, maybe “to be or not to be” is actually the most memorable literary phrase in English because it’s actually really, really bad writing.

In the chart below, the percentage of BE verbs used in each texts are listed (1 = most successful text). The differences seem pretty staggering, right?

Chart showing the percentage of be verbs in the marketing texts

Well, they would be staggering if I didn’t tell you that each horizontal axis line represents a half of a percentage point, or 0.5%. Now we can see that the differences between the texts, and especially between the best and worst texts, is practically non-existent. So much for being BE-free.

2. You can’t keep a good part of speech down

The second piece of advice is about the misuse of adjectives. According to some marketing/writing experts, copywriters should avoid using adjectives at all because “They are, in fact one of the worst [sic] elements of speech and even make a listener or reader lose trust”. Sounds serious. Except for the fact that linguists have long known that avoiding adjectives is not only bad advice but impossible to do, especially in marketing. How’s that? Well, first, this is another piece of advice which is given by people who can’t seem to follow it. But let’s say you’re trying to sell a t-shirt (or a car or a sofa or whatever). Now try to tell me what color it is without using an adjective. The fact is that different writing styles (sometimes called genres or text types), such as academic writing, fiction, or journalism, use adjectives to a different extent. Some styles use more adjectives, some use less, but all of them use adjectives because (and I can’t stress this enough) adjectives are a natural and necessary part of language. So writers should use neither too many or too few adjectives, depending on the style they are writing in.

But we’re here to run some tests. Let’s take the advice at face value and see if using less (or no) adjectives really means sales will increase.

Chart showing the percentage of adjectives in the marketing texts

Again, the differences in the results look drastic and again looks can be deceiving. In this case, the horizontal axis lines represents two percentage points (2%). The percentage of adjectives used in the three most successful and three least successful marketing texts are nearly identical. In fact, they are within two percentage points of each other. Another one bites the dust.

UPDATE August 22, 2013 – I’d like to mention that the use of modifiers, such as adjectives, is a good way of showing the depth of my research and what it can really offer marketers. While we saw that adjectives in general, or as a class, do not tell us much about which marketing texts will perform better, there are other ways to look into this. For example, there may be certain types of adjectives common to the successful marketing texts, but not found in the unsuccessful ones. Likewise, the placement of an adjective and whether it is preceded by, say, a determiner (the, an, etc.), may also be indicitave of more successful texts. And in a similar fashion, texts which use nouns as modifiers instead of adjectives may be more successful than those that do not. The important thing for marketers reading this to know is that I can research all of these aspects and more. It’s what I do.

3. It’s not all about you, you, you

The final piece of advice concerns the use of the word you, which is apparently one of the most persuasive words in the English language (see #24 on that page). Forget about the details on this one because I don’t feel like getting into why this is shady advice. Let’s just get right to the results.

Chart showing the percentage of the word you in the marketing texts

Does this chart look familiar? This time the horizontal axis lines once again represent a half of a percentage point. And once again, less than two percentage points separate the best and the worst marketing texts. In fact, the largest difference in the use of you between texts is 1.5%. That means that each one of the marketing texts I looked at – the good, the bad, and the in between – uses the word you practically the same as the others. It would behoove you to disregard this piece of advice.

So what?

I’ll admit that I picked some low hanging fruit for this post. But the point was not to shoot down marketing tips. The point was to show email marketers what corpus linguists (like me!) have to offer. Looking for specific words or adjectives is not the only thing that corpus linguistics can do. What if I could analyze your marketing and find a pattern among your more successful texts? Wouldn’t you like to know what it was so you could apply when creating copy in the future? On the other hand, what if there wasn’t any specific pattern among the more successful (or less successful) texts? What if something besides your copy predicted your sales? Wouldn’t you like to know that as well so you could save time poring over your copy in the future?

Really, if you’re an email marketer, I think you should get in touch with me (joseph.mcveigh@gmail.com). I’m about to start my PhD studies, which means that all my knowledge and all that corpus linguistics has to offer could be yours.

How about letting me analyze – and probably finding an innovative way to improve – your marketing? Sound like a good deal? If so, contact me here: joseph.mcveigh@gmail.com.

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