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  3. One day in 2008 an anonymous Twitter user posted a message: "I am certainly not bored, way busy! feel great!" That is all well a

One day in 2008 an anonymous Twitter user posted a message: "I am certainly not bored, way busy! feel great!" That is all well a

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问题     One day in 2008 an anonymous Twitter user posted a message: "I am certainly not bored, way busy! feel great!" That is all well and good, one might think, but utterly uninteresting to anyone besides the author and, perhaps, a few friends. Not so, according to Johan Bollen, of Indiana University Bloomington, who collected the tweet, along with plenty of others sent that day. All were rated for emotional content. Many proved similarly chirpy, scoring high on confidence, energy and happiness. Indeed, Dr Bollen reckons, on the day the tweet was posted, America’s collective mood perked up a notch. When he and his team examined all the data for the autumn and winter of 2008, they found that Twitter users’ collective mood swings coincided with national events. Happiness shot up around Thanksgiving, for example.
    The idea of tapping web-based data to build a real-time measure of users’ emotions and preferences is not new. Nor is that of using the results to predict their behaviour. Interest in internet forecasting was sparked by a paper published in 2009 by Hal Varian, Google’s chief economist. He found that the peaks and troughs in the volume of Google searches for certain products, such as cars and holidays, preceded fluctuations, in sales of those products. Other researchers have shown that searches for job-related terms are a good predictor of unemployment rates and that mentions of political candidates on Twitter correlate with electoral outcomes.
    Dr Bollen spotted another curious correlation. When he compared trends in the national mood with movements of the Dow Jones Industrial Average(DJIA)he noticed that changes in one of the mood measure’s seven components, anxiety, predicted swings in the share-price index. Spikes in anxiety levels were followed, around three days later, by dips in the price of shares. Why this happens remains unclear, but one possible explanation is that the falling prices were caused by traders’ tendency to exit risky positions when feeling strung up.
    Dr Bollen’s algorithm, which he described in a paper published in February in the Journal of Computational Science, has been licensed to Derwent Capital Markets, a hedge fund based in London. Derwent will use it to help guide the investments made with a ￡25m($41m)fund that the firm hopes to launch in the next few months. Other funds are rumoured to be using similar tricks already.
    All such initiatives face a problem, though. Humans excel at extracting meaning and sentiment from even the tiniest snippets of text, a task that stumps machines. To a computer, a tweet that reads "Feeling joyful after my trip to the dentist. Yeah, really" says that the author has been to the dentist and is now happy. Researchers have recently made strides in teaching machines to recognise such sarcasm, as well as double meanings or cultural references.
What does the quoted content of the last paragraph means?
选项 A、The algorithm of computers guided some fund companies’ investment.
B、Machines cannot tell the hidden meaning of people’s words.
C、The slang about dentist confuses computers.
D、People are good at analyzing the meaning and mood of a text.
答案B
解析 属信息推断题。题目问到最后一段文字中引号里的内容的含义，那么应该从引号这句话之前和之后的文字中寻找答案。前面的句子讲到人们很轻易就能从一些话中看出隐含的意思和情感，但机器却不能；后一句则意为人们已经开始努力教机器识别这样的讽刺用法，故可判断答案选B。选项A犯了无中生有的错误，与题目毫不相干，故错误。选项C犯了偷梁换柱的错误，句中的confuse非常具有迷惑性，但前面的slang about dentist文中根本没有提及，故错误。选项D犯了答非所问的错误，并没有回答题目所问的内容，故错误。
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