If you're ever reading a book or watching a movie and get the distinct feeling you've come across the story before – or even better, can predict exactly what's going to happen next –there could be a good reason for that.
如果你在讀書或者看電影時清晰地感覺到這個故事似曾相識——或者更厲害的，你能準(zhǔn)確預(yù)測出后面會發(fā)生些什么——嗯，這種感覺可不是毫無依據(jù)的。

Computer scientists have sifted through the language of more than 1,700 works of fiction and discovered that English literature consists of just six kinds of emotional arcs that make up nearly all of the most well-known stories.
計算機科學(xué)家們在測查了1700多部小說后，發(fā)現(xiàn)英語文學(xué)中只包含六種情感弧線，而幾乎所有的名著都是由它們構(gòu)成的。

While literary theorists have for centuries characterised and counted the basic plots and structures that writers use in stories, it's unlikely there's ever been such a rigorous scientific analysis of English fiction like this before.
盡管若干世紀(jì)以來，文學(xué)理論家們一直在研究作家寫故事時應(yīng)用的基本情節(jié)和結(jié)構(gòu)，分析它們的特征，歷數(shù)其種類，但好像此前從來沒有針對英語小說做過如此嚴(yán)謹(jǐn)?shù)目茖W(xué)分析。

Researchers from the Computational Story Laboratory at the University of Vermont mined the complete text of some 1,737 fiction works available on Project Gutenberg, an online collection of more than 50,000 digital books in the public domain. By analysing the sentiment of language used in chunks of text 10,000 words long in each of these texts, the researchers were able to register the emotional ups and downs for the stories as a whole. Negative words like "poverty", "dead", and "punishment" dragged the sentiment down, while positive terms like "love", "peace", and "friend" brought it up.
佛蒙特大學(xué)“計算機故事實驗室”的研究員們從古登堡計劃（Project Gutenberg是一個線上書庫，內(nèi)含5萬多本公版電子書）上找到了大約1737部全文小說，他們將這些文本分成文本塊，每個文本塊包含1萬個單詞，然后分析其中的語言情感，最終得出故事整體的情感起伏。“貧窮”、“死亡”、“懲罰”等消極詞匯會使情感變得低落，而“愛情”、“和平”、“友誼”之類積極詞匯會使情感變得高昂。

Doing this for over 1,700 books and charting the dynamics of each text, the team discovered that all stories basically boil down to one of a set number of emotional patterns. "We find a set of six core trajectories which form the building blocks of complex narratives," the authors write in their study.
研究團(tuán)隊在按照這種方法將1700多本書逐本分析、并畫出每本書的動態(tài)曲線圖之后，他們發(fā)現(xiàn)所有的故事最后基本上都會歸結(jié)到幾種情感模式中的一種。研究報告中寫道：“我們發(fā)現(xiàn)有6種核心的情感軌跡，它們是構(gòu)成復(fù)雜敘事大廈的磚瓦?！?/div>

According to the researchers, those six core emotional arcs are:
根據(jù)研究人員的說法，這6種核心情感弧線包括：

· "Rags to riches" (An ongoing emotional rise, eg. Alice's Adventures Under Ground)
“白手起家型”（持續(xù)的情感上漲，如《愛麗絲地下奇遇記》）

· "Tragedy, or riches to rags" (An ongoing emotional fall, eg. Romeo and Juliet)
“悲劇型”或者“家道中落型”（持續(xù)的情感下落，如《羅密歐與朱麗葉》）

· "Man in a hole" (A fall followed by a rise)
“穴人型”（先下落后上漲）

· "Icarus" (A rise followed by a fall)
“伊卡洛斯型”（先上漲后下落）

· "Cinderella" (Rise–fall–rise)
“灰姑娘型”（上漲－下落－上漲）

· "Oedipus" (Fall–rise–fall)
“俄狄浦斯型”（下落－上漲－下落）

Interestingly, based on download statistics from Project Gutenberg, the researchers say the most popular stories are ones that use more complex emotional arcs, with the Cinderella and Oedipus arcs registering the most downloads. Also popular are works that combine these core arcs together in new ways within one story, such as two sequential "Man in a hole" arcs stuck together, or the "Cinderella" arc coupled with a tragic ending.
有趣的是，研究人員說：根據(jù)從古登堡計劃下載的數(shù)據(jù)來看，最受歡迎的故事往往應(yīng)用了較為復(fù)雜的情感弧線，“灰姑娘型”和“俄狄浦斯型”囊括了大多數(shù)下載作品。另外，還有一些很受歡迎的作品是以一種新的方式將幾種情感弧線結(jié)合在一個故事里，比如說連續(xù)出現(xiàn)兩個“穴人型”，或者在“灰姑娘型”后面加上一個悲劇結(jié)尾。

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