Posts Tagged ‘positivity’

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?





































































smell sorry






















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?

















































































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


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



[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.)


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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A paper recently published in PNAS claims that human language tends to be positive. This was news enough to make the New York Times. But there are a few fundamental problems with the paper.

Linguistics – Now with less linguists!

The first thing you might notice about the paper is that it was written by mathematicians and computer scientists. I can understand the temptation to research and report on language. We all use it and we feel like masters of it. But that’s what makes language a tricky thing. You never hear people complain about math when they only have a high-school-level education in the subject. The “authorities” on language, however, are legion. My body has, like, a bunch of cells in it, but you don’t see me writing papers on biology. So it’s not surprising that the authors of this paper make some pretty basic errors in doing linguistic research. They should have been caught by the reviewers, but they weren’t. And the editor is a professor of demography and statistics, so that doesn’t help.

Too many claims and not enough data

The article is titled “Human language reveals a universal positivity bias” but what the authors really mean is “10 varieties of languages might reveal something about the human condition if we had more data”. That’s because the authors studied data in 10 different languages and they are making claims about ALL human languages. You can’t do that. There are some 6,000 languages in the world. If you’re going to make a claim about how every language works, you’re going to have to do a lot more than look at only 10 of them. Linguists know this, mathematicians apparently do not.

On top of that, the authors don’t even look at that much linguistic data. They extracted 5,000–10,000 of the most common words from larger corpora. Their combined corpora contain the 100,000 most common words in each of their sub-corpora. That is woefully inadequate. The Brown corpus contains 1 million words and it was made in the 1960s. In this paper, the authors claim that 20,000 words are representative of English. That is, not 20,000 different words, but the 5,000 most common words in each of their English sub-corpora. So 5,000 words each from Twitter, the New York Times, music lyrics, and the Google Books Project are supposed to represent the entire English language. This is shocking… to a linguist. Not so much to mathematicians, who don’t do linguistic research. It’s pretty frustrating, but this paper is a whole lotta ¯\_(ツ)_/¯.

To complete the trifecta of missing linguistic data, take a look at the sources for the English corpora:

Corpus Word count
English: Twitter 5,000
English: Google Books Project 5,000
English: The New York Times 5,000
English: Music lyrics 5,000

If you want to make a general claim about a language, you need to have data that is representative of that language. 5,000 words from Twitter, the New York Times, some books and music lyrics does not cut it. There are hundreds of other ways that language is used, such as recipes, academic writing, blogging, magazines, advertising, student essays, and stereo instructions. Linguists use the terms register and genre to refer to these and they know that you need more than four if you want your data to be representative of the language as a whole. I’m not even going to ask why the authors didn’t make use of publicly available corpora (such as COCA for English). Maybe they didn’t know about them. ¯\_(ツ)_/¯

Say what?

Speaking of registers, the overwhelmingly most common way that language is used is speech. Humans talking to other humans. No matter how many written texts you have, your analysis of ALL HUMAN LANGUAGE is not going to be complete until you address spoken language. But studying speech is difficult, especially if you’re not a linguist, so… ¯\_(ツ)_/¯

The fact of the matter is that you simply cannot make a sweeping claim about human language without studying human speech. It’s like doing math without the numeral 0. It doesn’t work. There are various ways to go about analyzing human speech, and there are ways of including spoken data into your materials in order to make claims about a language. But to not perform any kind of analysis of spoken data in an article about Language is incredibly disingenuous.

Same same but different

The authors claim their data set includes “global coverage of linguistically and culturally diverse languages” but that isn’t really true. Of the 10 languages that they analyze, 6 are Indo-European (English, Portuguese, Russian, German, Spanish, and French). Besides, what does “diverse” mean? We’re not told. And how are the cultures diverse? Because they speak different languages and/or because they live in different parts of the world? ¯\_(ツ)_/¯

The authors also had native speakers judge how positive, negative or neutral each word in their data set was. A word like “happy” would presumably be given the most positive rating, while a word like “frown” would be on the negative end of the scale, and a word like “the” would be rated neutral (neither positive nor negative). The people ranking the words, however, were “restricted to certain regions or countries”. So, not only are 14,000 words supposed to represent the entire Portuguese language, but residents of Brazil are rating them and therefore supposed to be representative of all Portuguese speakers. Or, perhaps that should be residents of Brazil with internet access.

[Update 2, March 2: In the following paragraph, I made some mistakes. I should not have said that ALL linguists believe that rating language is an notoriously poor way of doing an analysis. Obviously I can’t speak for all the linguists everywhere. That would be overgeneralizing, which is kind of what I’m criticizing the original paper for. Oops! :O I also shouldn’t have tied the rating used in the paper and tied it to grammaticality judgments. Grammaticality judgments have been shown to be very, very consistent for English sentences. I am not aware of whether people tend to be as consistent when rating words for how positive, negative, or neutral they are (but if you are, feel free to post in the comments). So I think the criticism still stands. Some say that the 384 English-speaking participants is more than enough to rate a word’s positivity. If people rate words as consistently as they do sentences, then this is true. I’m not as convinced that people do that (until I see some research on it), but I’ll revoke my claim anyway. Either way, the point still stands – the positivity of language does not lie in the relative positive or negative nature of the words in a text (the next point I make below). Thanks to u/rusoved, u/EvM and u/noahpoah on reddit for pointing this out to me.] There are a couple of problems with this, but the main one is that having people rate language is a notoriously poor way of analyzing language (notorious to linguists, that is). If you ask ten people to rate the grammaticality of a sentence on a scale from 1 to 10, you will get ten different answers. I understand that the authors are taking averages of the answers their participants gave, but they only had 384 participants rating the English words. I wouldn’t call that representative of the language. The number of participants for the other languages goes down from there.

A loss for words

A further complication with this article is in how it rates the relative positive nature of words rather than sentences. Obviously words have meaning, but they are not really how humans communicate. Consider the sentence Happiness is a warm gun. Two of the words in that sentence are positive (happiness and warm), while only one is negative (gun). This does not mean it’s a positive sentence. That depends on your view of guns (and possibly Beatles songs). So it is potentially problematic to look at how positive or negative the words in a text are and then say that the text as a whole (or the corpus) presents a positive view of things.

Lost in Google’s Translation

The last problem I’ll mention concerns the authors’ use of Google Translate. They write

We now examine how individual words themselves vary in their average happiness score between languages. Owing to the scale of out corpora, we were compelled to use an online service, choosing Google Translate. For each of the 45 language pairs, we translated isolated words from one language to the other and then back. We then found all word pairs that (i) were translationally stable, meaning the forward and back translation returns the original word, and (ii) appeared in our corpora in each language.

This is ridiculous. As good as Google Translate may be in helping you understand a menu in another country, it is not a good translator. Asya Pereltsvaig writes that “Google Translate/Conversation do not translate. They match. More specifically, they match (bits of) the original text with best translations, where ‘best’ means most frequently found in a large corpus such as the World Wide Web.” And she has caught Google Translate using English as an intermediate language when translating from one language to another. That means that when going between two languages that are not English (say French and Russian), Google Translate will first translate the word into English and then into target language. This represents a methodological problem for the article in that using the online Google Translate actually makes their analysis untrustworthy.


It’s unfortunate that this paper made it through to publication and it’s a shame that it was (positively) reported on by the New York Times. The paper should either be heavily edited or withdrawn. I’m doubtful that will happen.


Update: In the fourth paragraph of this post (the one which starts “On top of that…”), there was some type/token confusion concerning the corpora analyzed. I’ve made some minor edits to it to clear things up. Hat tip to Ben Zimmer on Twitter for pointing this out to me.

Update (March 17, 2015): I wrote a more detailed post (more references, less emoticons) on my problems with the article in question. You can find that here.

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