낙천 긍정 언어는 스페인어 영어, 슬픈 부정 언어는 러시아 한국어 In 10 languages, happy words beat sad ones

Peter Dodds (left) and Chris Danforth, mathematicians at the University of Vermont, led 

a study that confirms the 1969 Pollyanna Hypothesis that there is a universal human 

tendency to “look on (and talk about) the bright side of life.” Original photo: University of 

Vermont; adaptation: The Why Files

edited by kcontents 

케이콘텐츠 

미국 버몬트 대학 연구진들이 트위터, tv, 노래, 언론, 영화 등에 자주 사용되는 인기 단어 10만 단어를 분석했다. 분석 과정에서 긍정적 경험을 전달하는데 더 많이 사용되는 언어와 부정적 경험을 전달하는데 더 많이 쓰여지는 언어가 있는 현상을 발견하게 됐다. 전체 세계 10개 언어를 대상으로 긍정적인 표현에 더 많이 쓰이는 언어 조사에 착수했다. '행복' 수준 측정을 위해 언어감정지수 9점 만점 시스템을 도입했다. 그 결과 긍정적 감정들은 대개 스페인어, 브라질어, 포르투갈어, 인도네시어어, 영어로 조사됐고, 부정적 감정들은 러시아어, 한국어, 중국어로 표현되는 것으로 연구 결과가 나왔다. [출처=SPUTNIK 한국어방송]: http://kr.sputniknews.com/korean.ruvr.ru/news/2015_02_18/282956603/

How does word use change across the pages of the classic “Moby-Dick”? The unsettling 

conclusion is clearly evident in this analysis of word use. Modified from original graph by Hedonometer

Amid the everyday storm of flaming, bitching, cursing and general bad-mouthing in music and film, and on Twitter and the web, how’s this for bizarre? A new study of billions of words actually used in 10 major languages finds that writers in each language prefer positive, happy words. Whether it’s tweets in English, movie subtitles in Korean, books in Russian or websites in French, this general preference for positivity showed up in every single realm. The study is a classic example of using big data to address longstanding questions. The authors cast a wide net, looking at English, Spanish, French, German, Brazilian Portuguese, Korean, Chinese (simplified), Russian, Indonesian and Arabic. All told, they drift-netted 24 subtypes of media, including books, news outlets, social media, websites, television and movie subtitles, and music lyrics. Each medium provided at least 5,000 words, and each language, at least 10,000. “Lists that were used in the past were created by experts,” says Peter Dodds, who collaborated with Chris Danforth on the study. Both men are mathematicians at the University of Vermont’s Computational Story Lab. “We thought, ‘Let’s go find all the words we use most commonly.'” The researchers then asked 1,900 native speakers of the various languages to rate the words on a scale of 1 (illustrated with a deeply frowning face) to 9 (a broadly smiling face). The result was 50 ratings for each word, for 5 million total evaluations. But what about this? They asked whether words removed from their context could be misleading. Wouldn’t the first word of the love ballad “Killing Me Softly With His Song” register as negative? Yes, Dodds says, “but they wash out when the sample gets large enough. It’s like measuring the temperature of a room. If you look at a few molecules, maybe they are more or less active,” but temperature is an average of all the molecules. In the same way, “We are trying to get at the whole picture” of language, he says. Neutral words, such as “the” and “for” were ranked, as expected, around 5, but why not just ignore them? “We wanted the instrument we created to fit the language we would be using it on,” says Dodds. It’s easy to remove these “function words” from evaluations later on, he says, “but we can do this in a principled way. We don’t bring in our biases. We let people tell us what their language is.” Once the words and the ratings were nailed down, it was a straightforward — if big — computing task to rate the words actually used in different media for the various languages. And here’s the weird part: The average rating for every language — and every medium — was significantly above 5. So did the study show that people have, in general, positive emotions? “That’s strong,” Dodds says. “I think it’s proof of a positive bias in language, and language is our code for how we interact; it’s our great social technology, an amazing invention that allows our communication in a powerful way.” A long debate in linguistics boils down to this: Language shapes us. Or we shape language. The reality, Dodds says, is somewhere in the middle. “I think language encodes our sociality. We are social beings. You can argue that we are selfish or altruistic, but language tells a story about how we behave.” The 2011 geography of happiness — or at least, happy word usage, in Twitter. Dodds et al. 2011 The sweet tweet After collecting about 100 billion words in tweets, the researchers were able to track emotional expressions by time and place (if the tweets were geotagged). And that showed how emotional expression can vary from day to day. On a weekly cycle, Saturdays proved most positive; Tuesdays were most negative. Holidays, especially “merry” Christmas and “happy” Thanksgiving, marked the biggest spikes; deaths, murders and other outrages brought the biggest lows. Spanish — taken from Mexican websites, books and tweets — was the most positive — and Chinese, taken from Google books, was the most negative. With those consistent trends, the study confirmed the 1969 “Pollyanna hypothesis,” which held that most human communication tends toward the positive. Among cities, Boulder, Colo., rates highest for positive word usage while Racine, Wis., was the most negative. In Racine, Dodds says, “There is a lot more swearing, and ‘don’t,’ ‘never,’ ‘no,’ ‘nobody,’ and less ‘haha’ — that’s important on Twitter — and less ‘happy,’ ‘best,’ and ‘awesome.'” We returned to our attempt to summarize the study, asking, “Are you saying people are happy based on the words they choose?” No, says Dodds. “We are not telling you what people are thinking inside their heads. We are telling you how people react to the words people use.”  http://whyfiles.org/2015/in-10-languages-happy-words-beat-sad-ones/

A time series of positivity and negativity in Twitter posts in the U.S. since January 2014. Holidays such as Christmas and Thanksgiving stand out as the happiest days, while the Ferguson, Mo., protests and the death of Robin Williams were the saddest days. Note the fine-scale weekly “wobble” of happiness between week’s beginning and week’s end, and the fact that the rating is consistently above a neutral 5. 

edited by kcontents

"from past to future"

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