時尚雙語:計算你的“樂觀率”

In his famous book Learned Optimism, Martin Seligman points out how our present use of language can be a fairly accurate predictor of future success. Seligman explains how he was able to predict outcomes of sporting events with reasonable accuracy by comparing the language used by the coaches and players in interviews before the event. Basically what he did was count all the positive words and the negative words in published pre-game quotes from the players and coaches, and then he calculated the ratio of positive words to negative. The team with the higher ratio was the one picked to win. There is some subjectivity in deciding whether a word is positive, negative, or neutral, but if you try it yourself, I think you’ll find that most of the time it’s fairly easy to classify words. Seligman also explains using a similar process to predict the winners of political elections.

Try this for yourself. Here’s a sentence I grabbed from Yahoo News on Feb 23:

Scientists fear the avian flu that has killed 46 people in Asia could be the strain that will cause the next global pandemic but said more evidence is needed about how infectious it is in humans.

How many positive and negative words do you count? I count zero positive and four negative (fear, killed, pandemic, infectious). So this sentence has a ratio of 0/4 = 0.

Let’s try the same process on all of the headlines from Yahoo News (I’m using the Feb 23 version). I count 6 positive words (eases, adds, new, found, right, wealthy) and 15 negative words (denounce, fight, die, soak, death, somber, slain, fears, concerns, dismissed, defiant, avoids, risk, pandemic, handouts) for an overall ratio of 6/15 = 0.4.

My picks are subjective of course, so yours may be different, but try it for yourself on any news site. If you find one with a ratio above 1.0, please tell me about it!

Try this on yourself as well. Go over some text you wrote recently — emails, forums posts, whatever. What’s your ratio of positive/negative words? Seligman would argue that this is a powerful predictor of future success. Some personal development experts believe that by intentionally choosing more optimistic words in the language you use, you’ll start to become more optimistic in your thinking, which will in turn lead to better results. Anthony Robbins has a whole chapter about it in one of his books; he refers to it as “transformational vocabulary.”

Have some fun and try this on your friends and co-workers. Grab something they wrote, and compute their ratio. Is their language predominantly optimistic (>1.0) or pessimistic (<1.0)? Who has the highest score? The lowest score? Any interesting patterns?

What kind of boss do you work for? What about your company’s brochures? If you run your own business how’s your marketing material, your web site, your business plan? Are you projecting confidence or self-doubt to your customers? What about your journal entries? Your to do list?

You’ll often see a pattern where like attracts like. Pessimistic news sources will attract pessimistic readers, partly because those are the best targets for advertising — negative people are more likely to believe that buying products will change their emotional state. A pessimistic company will attract and breed pessimistic employees — the high-energy positive people will go where their enthusiasm is welcome. So there’s a good chance you’ll see similar ratios to your own when you look around your environment.

馬丁·賽裏格曼在他那本著名的《習得的樂觀》中指出，我們目前使用語言的方式可以相當準確地預測未來能否成功。賽裏格曼解釋了他是如何通過比較教練和隊員在賽前接受採訪時使用的詞語來較爲準確地預測比賽結果的。基本上，他所做的就是根據賽前隊員和教練發佈的談話，算出其中所使用的積極和消極詞彙的數量，然後用積極詞數比上消極詞數得出樂觀率。樂觀率較高的那個隊將會贏得比賽。在確定一個詞是積極、消極還是中性時具有少許主觀性，但如果你自己試試，我想你也會發現，大多數時候，給詞彙分類還是比較容易的。賽裏格曼也說明了如何用相似的方法來預測政治選舉中的勝出者。

你來做下面這個試驗。這是我從2005年2月23日的雅虎新聞上摘抄的一句話：

“科學家們擔心，已經導致亞洲46人死亡的禽流感是否會成爲下一次全球瘟疫的源頭，但該病毒對人類的傳染性還需更多的證據來證實。”

你數出了多少個積極詞和消極詞？我數出了0個積極詞，4個消極詞（擔心，死亡，瘟疫，傳染性）。所以這句話的樂觀率是0/4=0。

我們試試用同樣的方法來計算所有雅虎新聞上的標題（我用的是2005年2月23日的版本）。我數出了6個積極詞（安定、增加、新穎、發現、正確、財富）和 15個消極詞（譴責、戰爭、死亡、浸泡、屠殺、憂鬱、殺害、恐懼、擔憂、撤職、挑釁、逃避、風險、瘟疫、救濟），因此樂觀率是6/15=0.4。

我的摘抄當然是主觀的，所以你的結果可能有所不同，但你可以到任何的新聞網站去試驗一下。如果你發現了樂觀率超過1.0的的新聞網站，可別忘了告訴我！

同樣，也做做下述的試驗。瀏覽一下你最近寫的東西——電子郵件、論壇的帖子，隨便什麼。你詞彙的樂觀率是多少？賽裏格曼聲稱這是預測未來能否成功的有力證據。某些個人發展專家確信，在言辭中有意選擇積極的詞彙，能讓你的思想變得更加樂觀，最終帶來更好的成果。安東尼·羅賓斯在他的一本書中有整整一章是專門闡述這一點的；他把這稱之爲“轉變的詞彙表”。

找點樂子，在你的朋友和同事身上試試。隨便找些他們寫的東西，計算其樂觀率。他們的語言是樂觀主義（>1.0）還是悲觀主義（<1.0）的？誰的樂觀率最高？誰的樂觀率最低？發現了什麼有趣的模式嗎？

你在爲怎樣的老闆工作？你公司的手冊寫得怎麼樣？如果你經營自己的事業，你的營銷資料、網站、商業計劃又如何？你向你的客戶表達了自信還是自我懷疑？你的日誌呢？你的任務列表呢？

你常會看到一種物以類聚的模式。悲觀的新聞常吸引悲觀的讀者，部分是因爲這類人通常是廣告的最佳目標——消極的人更可能會相信，通過購買產品可以改變他們的情緒狀態。一個悲觀的公司會滋生和引來悲觀的僱員——那些精力充沛、積極向上的人會到那些歡迎他們的熱情的地方去。因此，當你觀察自身周邊環境的樂觀率時，基本上也就能瞭解你自己的樂觀率了。