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The emotional profile of words

The emotional profile of words. Tyler Schnoebelen Stanford University http://www.stanford.edu/~tylers/emotions . Welcome!. If you’re reading the presentation online, please make sure to check out the notes fields—they have content that supplements/explains the content of the main slides.

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The emotional profile of words

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  1. The emotional profile of words Tyler Schnoebelen Stanford University http://www.stanford.edu/~tylers/emotions

  2. Welcome! • If you’re reading the presentation online, please make sure to check out the notes fields—they have content that supplements/explains the content of the main slides. • Any questions or comments? Please send them to me! • TylerS at Stanford dot edu.

  3. Some claims • Linguistics is about understanding human beings. • To understand human beings is to understand the variety and complexity of emotional experiences they have. • Linguistics can offer a lot by showing how linguistic resources are used in creating and coping with these experiences.

  4. But…

  5. Referential

  6. Persuasive

  7. Expressive

  8. Goals • Immediate goal: • What are the conceptual tools and actual methods to analyze language in terms of emotion? • Bigger goals: • What is the structure of the affective lexicon in English? • What about other languages?

  9. Classifying

  10. Classifying • Classification structures our body of knowledge. • Especially indispensable when it’s not clear where to begin.

  11. Typical sentiment analysis

  12. Approach, assumptions, hypotheses • Words are not just positive/negative • Sentences are not just objective/subjective, either • Words are better characterized by the types of emotional work they do • Items that occur in similar situations are alike • Though not in ways we understand yet • Placement in the network dictates which semantic shifts are possible

  13. Not markers…

  14. Not markers…makers

  15. The Experience Project • 31,675 “confession” stories • Tagged by readers with 27,187 tags • I understand 11,277 • You rock 3,781 • Sorry, hugs 3,733 • Teehee 3,545 • Wow, just wow 916

  16. An example • Are you missing me? Do you wish you could reach out and touch my face. Are you tossing and turning, or pacing back and forth? You know where I am and that I am alone. But what you may not know is how much I need you.

  17. Monroe et al 2009 • Log-odds with Bayesian priors • for a given word, • in a given topic, • do Democrats/Republics use it a lot more • taking into consideration how they use words OUTSIDE that topic

  18. “Teehee” • Funny topics • naked • poop • snoring • toilet • Metacues • ! • ha/haha/hahaha • lol • oh • omg • Appraisals • cool • funny • cute • naughty • cow • hot • loud • weird

  19. Other categories “Sorry, hugs” “I understand” feel hate wish • cancer • died • sorry “You rock” • amazing • angel • beautiful

  20. But is there structure? • There are 111 uses of omg • There are 2,332,769 tokens total, so omgmakes up 4.75e-05 of the data • There are 456,454 words marked for “hugs”, so if everything was random we’d expect: • 456,454 * 4.75e-05 = 21.72 occurrences of omg in the “hugs” category • Actually, there are 14—smaller than we’d expect • Is it significantly smaller? We use a g-test:

  21. Restrictions • I restrict myself to words that have 10 or more instances overall • Observed/Expected scores that are unusual • .5 standard deviation outside the mean for at least one category of a word • Are statistically significant by g-test • POS tagging keeps adjectives, adverbs, and discourse items (wow, omg, !, yup) • 1,297 words

  22. Constraints • We’ll set wow, just wow aside due to sparseness of the data and focus on the other four categories. • If we reduce down to just over-representation, under-representation and “as expected”, there are logically 3^4=81 patterns • Well, “-1, -1, -1, -1” and ‘1, 1, 1, 1” aren’t actually possible • And the technique describe above gets rid of “0, 0, 0, 0”. • But if all things were equally, we would still have 78 patterns

  23. We don’t • Instead of 78 patterns, there are 42 • Nor are words evenly divided

  24. IMDB—Internet Movie DataBase • 45,772 movies • 1.36 million reviews • 29 genres • 1-10 stars • (And “no rating”)

  25. Structure of stars

  26. Probabilities • Similar to what we did with the Experience Project, but an extra layer: • Expected=p(genre)*p(star-rating)*p(word)*total corpus size • Restricted to 12 genres that don’t overlap • Combined with Experience Project data

  27. Experience Project + IMDB • 812 words overlap • ugh • terrible • sucks • weird • lame • cute • interesting • meaningful • sooo • emotional

  28. Enthusiasm Trop bien, trop fort, excellent

  29. Surprise

  30. Rejection Pote, mongars, mec, putain

  31. Words can be infected… • Semantic dynamics, changes of meaning • E.g., semantic contempt can creep in to things from their use (Kaplan 1999)

  32. Baggage

  33. Not markers…makers

  34. 3 types of emotional meaning • Valence—positive/pleasant vs. negative/unpleasant • “happy” vs. “sad” • “psyched” vs. “disappointed” • Me-ness • Primarily about the speaker, identity work • Do ideophones go here, perhaps? • Us-ness • Relational—moving interlocutors closer or further away • Most intense emotional reactions will happen here

  35. Us-ness • Examples: • Pitch in Zapotec (Sicoli 2007) • Breathiness in Japanese (Campbell 2004) • Kinyarwanda diminutives • Diphthongs in Czech • Palatalization in Yiddish and Basque • Impersonating your listener

  36. Us-ness • Every context has a valence, it’s quickly set and assessed • Positive valence + us-ness: • Intimacy • Negative valence + us-ness: • Pejoration • Teasing and joking can bring us closer—or push us apart.

  37. What does this mean for fieldwork? • Most African languages don’t have enormous corpora like English • But the “observed vs. expected” thinking will keep you attuned to many different features, even if your counts are low. • And the patterns in Well-Studied-Languages-with-Giant-Corpora can serve as a template • What are the linguistic resources (lexical items as well as pitch, syntax, etc) that are used to convey these meanings? • How do valence, me-ness, and us-ness get conveyed? • What is the distribution of items in different categories? Are the categories themselves different?

  38. What I’m hoping for • You’ll get interested in adding emotion to your own fieldwork • You’ll tell me what you’ve already observed • (Especially about diminutives, by the way)

  39. Thanks for listening! http://www.stanford.edu/~tylers/emotions

  40. A world experienced without any affect would be a pallid, meaningless world. We would know that things happened, but we could not care whether they did or not. (Tomkins, 1995, p. 88)

  41. Some questions • Don’t worry about assigning a particular emotion—broadly speaking is a positive or negative emotion being communicated? • How explicit are the emotions? If the utterance isn’t as explicit as “I am happy”, does it indicate behavior or disposition (the difference between laughing at something and liking something)? • Is the emotion directed (the girl liked the teacher) or more mood-like (the girl was happy)? With ideophones, I suspect the object is often what the emotion is directed at. • How intense is it? (Like vs. love vs. adore)

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