Affect and power

14 Jul

This is an ambitious post in its scope but rather loose in its structure. I want to connect affect/emotion, power, police, Stand Your Ground laws, sentiment analysis, marketing, digital humanities, and Sharknado. Underneath, I’m thinking about how expressions of emotions that we may exhibit or witness are not so much internal states made visible as they are kinds of social positionings. (You can check out my dissertation, “Emotions are relational: Positioning and the use of affective linguistic resources” if you want to see the assumptions/data/findings behind a lot of this, it’s also much more corpus linguistics than you’ll get out of this particular post.)

Police states

I want to begin by asking you to go off to The Moth to listen to Steve Osbourne, an NYPD cop, talk about doing his job. But I’ll try to make this work even if you don’t.

SteveOsborne_profile

A huge part of Osbourne’s story is answering the question, “How do you deal with all the feelings and still do what you do?” How do you see young men dead in the street and keep going? It’s something he addresses specifically and returns to throughout his narrative. Here’s how he conceptualizes it, breaking out of the narration of events on a Tuesday morning at home with his wife. That is, Osbourne finds it necessary to interrupt the order of events to make sense of them via a lengthy metacomment. This is an important way that emotions come to us in texts—not just in the reporting of events but in the present-tense evaluation of them.

And the way we do it is, you learn, very early how to shut it down. You learn how to turn off your feelings and you learn how to be professional. You learn how to do your job….Everybody thinks that, like, we build a wall between us and the public, that’s not necessarily true, what we learn to do is build a wall between ourselves and our feelings. And that’s how you stay focused, that’s how you stay professional, and that’s how you do your job. [6:03-6:42]

The idea of shutting things down, turning them off is crucial for Osbourne. So is the idea of the doing his job and being professional—that’s why and how he leaves his wife on an emotional morning. The truth is that his account of the takedown day is suffused with emotion and it would be useful to map out which ones are compatible with professionalism and which are not. Although he lumps all feelings together in his explicit words, he’s clearly not actually lumping them together over the course of the narration. But the way the narrative works is to explain how it’s required to become like stone and how cracks appear. Eyes-welling-with-tears count as cracks, but not every emotion that is implied seems to count as a crack. This suggests the importance of thinking through emotional management/regulation. Situations, identities, choices, institutions, and selves are constructed/maintained/perturbed by which types of emotions are acceptable, when, and for how long. Emotional regulation is also clearly structuring why/how/when evaluations pop up in narratives.

For Osbourne, doing his job requires shutting down feelings. He has an idea of “cop mode” being something not-human. (Implicit: feelings are human, more explicit: feelings get in the way of institutional/exceptional/quotidian duties.)

I knew that eating, like, the physical act of eating, like, putting food in your mouth, makes you feel like a human being. And I had no time for that. I had no time to be a husband, I had no time to be a human being, I was in that cop mode, you know [8:28-8:42]

This notion also reappears and, in fact, the story ends with consuming free food from a McDonald’s tent and trying to hide tears from the other cops when he sees a 5 year-old’s thank you note in his happy meal. In the story, Osbourne refuses his wife’s sandwich but accepts the McDonald’s burger. He loves free food. He feels that the McDonald’s tent shows that someone cares. Someone has thought: rescue workers need to eat. You could really go crazy with connections between consumption, the body, emotion, home, relationships, law/order, and the costs of citizenship.

Note also that we are talking about a story that is recorded and propagated through “The Moth”, which is meant to promote story-telling. Featured stories are unlikely to be randomly chosen—they likely involve that magic combination of telling people something they already know alongside some unexpected turns. Having said that, I’m not sure any of the turns in Osbourne’s story are all that unexpected and my guess is that his accent is part of what makes him compelling to both listeners and selectors-of-featured-stories-within-The-Moth. In other words, there’s a story of circulation, citationality, style and emotion here to tell, too. And I probably shouldn’t leave out class. Class, occupation, accent. Gender is part of the story and sexuality, too. We drift into how intersectional all these categories are.

Access to affect is not randomly distributed. Everyone moves around between various social position (we move ourselves, we are moved by others) but these social positions have different affective foot patrols structuring who feels what when. And we see this start-to-stop through the George Zimmerman trial (easy to slip and call it the “Trayvon Martin trial”, isn’t it?). In taking an affective focus we may turn away from the role of institutions. But these institutions structure and are structured by affects. Who is allowed to feel what when. Who gets to be afraid, who doesn’t? What emotions are “reasonable”? Stand Your Ground laws are built upon and build out differences in permitted emotions.

BPGa2YBCAAA8U1o.jpg-large

Read http://ow.ly/1ZfaNa for more; this is from the Urban Institute Justice Policy Center’s research on murders being found justifiable (SYG indicates states with Stand Your Ground laws)

I am reminded of John Gaventa’s work on power. It is surely an exercise of power to force a group of coal miners to stop striking. But there is something even more striking about the kind of power that prevents miners from even considering a work stoppage. Individuals and structures create marginal people and this is surely connected to the creation of marginal feelings. Feminist scholars considering definitions of power have wondered about adding “power to do something” to the standard definition of “power over something/someone”. In an affectively oriented research program, we might see power as the ability to block and transform feelings—in which case, the need to enter into cop mode shows power being exercised through Steve Osbourne. The feelings that a young man being hunted would feel would also be evidence of power. What feelings get marginalized in your own recountings? What feelings do you never even feel in the first place? (Here’s my longer essay on power, fwiw, dense with references.)

As I close out this section, consider the difference in our understanding of George Zimmerman’s various affective states and the ones that Steve Osbourne relates. For Osbourne, the personal need to shut down feelings is connected to a job, which is (I am presuming) connected to ideas of maintaining order and justice. This is not how Zimmerman seems to conceptualize his experience nor is it how we “read” him.

In a corpus linguistics blog, I should properly show to what degree Osbourne’s (or Zimmerman’s) narratives, metaphors, etc. are shared by others. I’m not going to do that, but a couple observations in that direction.

  • The evocative shut it down also comes up in a number of media interviews of people in law enforcement in COCA. There it can mean stopping an investigation (an internal matter) or stopping criminal activity (an external matter). It’d be interesting to look across oral histories or other data sources and build connections between these kinds of uses and the emotional kind. In Osbourne’s story, shutting his emotions down is part of his being able to do his work. But emotional regulation seems to also be connected to audiences like his wife and the people he works with.
  • Paul Ekman’s work on emotion has focused on the face, it seems that when people talk about being “like stone”, they are often talking about the face. The edifice metaphor is about nothing going on behind the hardness—or at least no signal except for “impenetrable”. Is there a propensity for this to be about what others see or about what the individual feels? Or is this even a meaningful division?

Marketing and sentiment analysis

After I added the Trayvon Martin content, I recognized that I really ought to just cut this section. Surely it is too trivial to go alongside important issues of justice. I am leaving it in because it may suggest how sentiment analysis projects could be more than buzz metrics. They could expand to complicate and clarify our notions of which individuals express which sentiments regarding which topics to which audiences. They could point us to affective social network analysis. Sentiment could be seen as not just a percentage to report but as part of a bigger process that structures the spread of information and sentiments themselves. But this is not a one-way street. Culture studies could likely benefit from the way (sophisticated) sentiment analysis tools model texts, pinpointing which specific elements convey which kinds of signal.

Sharknado trailer (Screengrab)

With that caveat, let’s take a recent tweet about the Syfy made-for-TV-movie, Sharknado, which whipped up a frenzy of activity on Twitter.

That’s friggin’ gross! And AWESOME! #Sharknado

Here’s how a sentiment analysis tool is basically going to deal with this—you decompose this into features. Somewhere there was training data that was coded. A model was built that made someone somewhat happy with the fact that when you held out parts of the training data and treated them as test data, the model didn’t get too many false positives or too many false negatives. That is, it had a reasonable level of accuracy.

A few notes:

  • Usually the features come down to individual words (the exclamation point will likely count as a word). More academic researchers will likely have bigrams, trigrams, maybe 4grams. That would be atypical in the kind of tools that a marketing person at Syfy would have access to.
  • A sophisticated system would come up with features from the data itself. But most commercial tools just involve keywords that were decided a priori to carry a signal.
  • Usually it’s just “positive, negative, neutral”, even though clearly our emotional lives are awash in more complicated states. For example teasing (potentially-positive-solidarity-through-negative) and ambivalence (both love and hate).
  • There’s clearly a signal in the all-caps. Probably most systems don’t use this.
  • Ideally, the training data is like the data you want to predict sentiment for. How useful is it to predict the sentiment of movie reviews based on book reviews or Twitter data based on Amazon data?
  • Genre plays a role beyond data source: gross in a romantic comedy is likely to be negative, while in a horror movie it’s likely to be positive.
  • How do you know what the sentiment about? Presumably it’s about some particular scene (like the hero chainsawing his way out of a great white shark). More generally, the hashtag #Sharknado is a pretty good clue.
  • Notice that the words chosen exist in a web of alternatives. The most obvious of these is friggin’ which seems to be avoiding a swear word. This choice would carry a social signal in the real world that is not disconnected from affect, though no tools I know about really know how to unite this kind of positioning with scoring sentiment.

Commercial sentiment analysis tools tend to give marketing professionals a “score” that they can track over time. Ideally, the tools give the marketing folks a way to understand what’s going wrong, what’s going right, how brand awareness is trending, and some way to actually take action to take advantage of opportunities and nip product problems in the bud. (Here a product could be a blender or a political candidate.)

To accurately capture the sentiment of the Sharknado-nado, though, we can’t really just look at individual tweets. We would need to appreciate that there was a broad group activity going on and expressed sentiments were not in a vacuum, they were part of a flurry and the delight of being caught up in that. (see also Bachorowski, Smoski, Tomarken, & Owren, 2004 on people laughing to movies more with others than alone). Participation in the #Sharknado event involved the ability to tweet, retweet, get retweeted. The ability to celebrate something terrible or take an oppositional stance. There was identity work and relationship work going on. The construction of sentiment is bigger than just “you have four positive words, two negative words, and five neutral words; based on the following weightings you feel positively”. Current systems do not know what individual tweeps’ emotional regulation schemes are (if everything I tweet about is AWESOME, is it really all that awesome? Maybe but it’s not as obvious as if I have a broader distribution of evaluations). Modeling and computing an enriched sense of context is not easy. But there is an implicit assumption that the data is disconnected from its author, its audience, and other patterns. It is not.

Some concluding thoughts

I believe these are rich sites for investigation and that I have not done them justice in this space. But I would like to say that cultural theory about emotion often seems to engage in very sophisticated ideas that are untethered from actual examples, even in digital humanities discussions. In this way, it’s not unlike the literature in computational linguistics about sentiment analysis: that literature is highly data driven, but probably suffers from being concerned more about precision/recall and feature definition than enquiry into what’s going on for the individuals whose data their findings are derived from. The former engages reproduction and power, the later can identify broad patterns and exceptions. There is interesting work ahead to combine them together. Alone, each can and does seem to float above the specific data it purports to be about. Combined together, I believe they would lead researchers back to specific processes, specific examples, specific patterns, and specific exceptions. I may be painting with too broad a brush. For the exceptional scholars, my apologies. I can see amazing possibilities right there on the horizon and I want to go to there.

Post-script

I was recently told a story about young girl whose mother put her on a good softball team. The girl was clearly the worst player on the team. This did not deter the mother who insisted her daughter play. Up to bat, the girl would shed tears with each strike and she had a lot of strikes. Everyone would look away. The story was told to me to point out that this is how we train each other when to express which emotions. But it clearly isn’t as simple as “everyone was reproducing ‘sports require toughness'”.

Some people may turn away in disapproval of the girl. But surely others of us would turn away because we know the family dynamics at play and we do not know what to do. Do you yell at the mother, do you go hug the daughter? The members of the crowd, too, are shaped by emotional regulation schemes that are broader than “what’s appropriate at a softball game”. How do you balance public facework, family sovereignty, and the like? It’s structuration: structures are built out of individual actions but those actions are structured by the actions that came before them.

These emotional regulation schemes are created, maintained, and perturbed by the stories we tell. But “a text” must be broadly construed. A story is recorded in words but during the event itself: wordless tears, wordlessly averted eyes. In the event itself, not the story of it, there are only the words strike on the field and a mother’s c’mon Sarah from the bleachers. Words are not the only speech acts, silences are, too. And those of us who make our living with words may give them a bit too much weight. They are not always the magical incantations of creation and destruction we imagine them to be. And not everyone has such imaginings. Nor do even the wordiest among us have them all the time.

Too much

Already, here below, has met its match.

Yet nothing’s gone, or nothing we recall.

And look, the stars have wound in filigree

The ancient, ageless woman of the world.

She’s seen us. She is not particular—

Everyone gets her injured, musical

“Why do you no longer come to me?”

To which there’s no reply. For here we are.

(James Merrill, The Book of Ephraim)

Horse name corpus for Belmont Stakes

6 Jun

The Belmont Stakes (the third of the Triple Crown) are happening this weekend. Check out this blog post on horse names:

http://idibon.com/back-the-right-horse-name/

Does anyone have an even more enormous corpus of horse names? I grabbed and analyzed the top 4 finishers for the last 137 years of the Kentucky Derby and showed the diversity of naming schemes.

Categorizing horse names for the last 137 years of the Kentucky Derby: see http://idibon.com/back-the-right-horse-name/

Opinionated tweets

28 May

Luo, Osborne and Wang make the following data set available:

https://sourceforge. net/projects/ortwitter/

They crawled 30 million English-language tweets and then had 7 people use a search engine to call up results. The results showed 100 tweets and the people had to classify each of the 100 for whether it was (a) opinionated about the query, or (b) not opinionated.

There were 50 queries resulting in 5,000 annotated tweets.

Read their paper here:

Opinion Retrieval in Twitter: http://homepages.inf.ed.ac.uk/miles/papers/icwsm12.pdf

GIF pronunciations and the CMU Pronuncing Dictionary

23 May

The CMU Pronouncing Dictionary offers us the chance to see how many ways there are to pronounce “g” in English. Should it be hard-g GIF or soft-g JIF? (There are 8+ pronunciations of “g”!)

http://idibon.com/gif-and-ways-to-say-g/

Crowdsourcing and corpus studies

23 May

One of the things you might want to use crowdsourcing for is to annotate or create corpora.

You can read about crowdsourcing techniques in linguistics in this paper:

Using crowdsourcing for linguistic research by Tyler Schnoebelen and Victor Kuperman

Or see a bunch of different linguistic research projects that used crowdsourcing (presented at the Linguistic Society of America’s annual conference):

LSA 2011 presentation

And you can read an analysis of using crowdsourcing to help assess damage from Hurricane Sandy here:

http://idibon.com/crowdsourced-hurricane-sandy-response/

Corpus linguistics and the NBA playoffs

21 May

In honor of the NBA Draft Lottery, some facts about the vagaries of three synonymous-looking terms: basketball, hoops, and bball.

http://idibon.com/bball-and-hoops-when-do-synonyms-matter/

Are basketball, bball, and hoops really synonyms? From http://idibon.com/bball-and-hoops-when-do-synonyms-matter/

 

 

Discovering linguistic diversity

20 May

Over at the Idibon blog a couple posts that talk about how languages do stuff.

First, some of our favorite things about indigenous languages of the US and Canada:

http://idibon.com/powwow-5-facts-about-native-american-languages/

(The origin of “powwow” and Havasupai pronoun fun, Cherokee verbs and more.)

And using a corpus of movie subtitles, an analysis of a single line from film noir in French, Hungarian, and Turkish.

http://idibon.com/the-multilingual-falcon/

The Maltese Falcon, analyzed in French, Hungarian, and Turkish at http://idibon.com/the-multilingual-falcon/

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