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.

马丁·赛里格曼在他那本著名的《习得的乐观》中指出，我们目前使用语言的方式可以相当准确地预测未来能否成功。赛里格曼解释了他是如何通过比较教练和队员在赛前接受采访时使用的词语来较为准确地预测比赛结果的。基本上，他所做的就是根据赛前队员和教练发布的谈话，算出其中所使用的积极和消极词汇的数量，然后用积极词数比上消极词数得出乐观率。乐观率较高的那个队将会赢得比赛。在确定一个词是积极、消极还是中性时具有少许主观性，但如果你自己试试，我想你也会发现，大多数时候，给词汇分类还是比较容易的。赛里格曼也说明了如何用相似的方法来预测政治选举中的胜出者。

你来做下面这个试验。这是我从2005年2月23日的雅虎新闻上摘抄的一句话：

“科学家们担心，已经导致亚洲46人死亡的禽流感是否会成为下一次全球瘟疫的源头，但该病毒对人类的传染性还需更多的证据来证实。”

你数出了多少个积极词和消极词？我数出了0个积极词，4个消极词（担心，死亡，瘟疫，传染性）。所以这句话的乐观率是0/4=0。

我们试试用同样的方法来计算所有雅虎新闻上的标题（我用的是2005年2月23日的版本）。我数出了6个积极词（安定、增加、新颖、发现、正确、财富）和 15个消极词（谴责、战争、死亡、浸泡、屠杀、忧郁、杀害、恐惧、担忧、撤职、挑衅、逃避、风险、瘟疫、救济），因此乐观率是6/15=0.4。

我的摘抄当然是主观的，所以你的结果可能有所不同，但你可以到任何的新闻网站去试验一下。如果你发现了乐观率超过1.0的的新闻网站，可别忘了告诉我！

同样，也做做下述的试验。浏览一下你最近写的东西——电子邮件、论坛的帖子，随便什么。你词汇的乐观率是多少？赛里格曼声称这是预测未来能否成功的有力证据。某些个人发展专家确信，在言辞中有意选择积极的词汇，能让你的思想变得更加乐观，最终带来更好的成果。安东尼·罗宾斯在他的一本书中有整整一章是专门阐述这一点的；他把这称之为“转变的词汇表”。

找点乐子，在你的朋友和同事身上试试。随便找些他们写的东西，计算其乐观率。他们的语言是乐观主义（>1.0）还是悲观主义（<1.0）的？谁的乐观率最高？谁的乐观率最低？发现了什么有趣的模式吗？

你在为怎样的老板工作？你公司的手册写得怎么样？如果你经营自己的事业，你的营销资料、网站、商业计划又如何？你向你的客户表达了自信还是自我怀疑？你的日志呢？你的任务列表呢？

你常会看到一种物以类聚的模式。悲观的新闻常吸引悲观的读者，部分是因为这类人通常是广告的最佳目标——消极的人更可能会相信，通过购买产品可以改变他们的情绪状态。一个悲观的公司会滋生和引来悲观的雇员——那些精力充沛、积极向上的人会到那些欢迎他们的热情的地方去。因此，当你观察自身周边环境的乐观率时，基本上也就能了解你自己的乐观率了。