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Sentimental journey
情感旅程
A new way to review scientific literature is being tested
一種正在試驗中的審查科學文獻的新方法
It analyses the sentiments of papers』 authors
它分析論文作者的情感
文章大概介紹：
本文介紹了一種新的分析大量文獻的方法，通過分析大量保護動物話題論文中作者所表現出的情感，來觀察近年來的保護瀕危動物的工作是否有所作用。通過這種方法，發現論文作者的情感由消極轉為積極，這說明保護動物是有成果的。不過由於保護動物本身就是一種由情感驅動的人類行為，所以這種方法行之有效，能不能運用到其他學科還有待商榷。但是已經有很多人從此分析方法中獲益。
How do you measure progress? That is the question Kyle Van Houtan, an ecologist at the Monterey Bay Aquarium, in California, found himself asking when he faced the task of working out whether methods of boosting the populations of endangered species in the wild have improved over the years.
如何衡量進步？這是加州蒙特利灣水族館的生態學家凱爾·范·霍坦在面對增加野生瀕危物種數量的方法多年來是否有所改善的任務時，問自己的一個問題。
In normal circumstances, those keen on studying the effectiveness of research write reviews of the scientific literature. In a flourishing field, though, this may involve reading and extracting information from hundreds, possibly thousands, of papers. That requires a large team, and brings problems of co-ordination. Dr Van Houtan therefore wondered whether getting computers to do the heavy lifting might help.
通常情況下，那些熱衷於研究效率的人會對科學文獻發表見解。然而，在一個蓬勃發展的領域裡，這可能涉及到閱讀成百上千的論文，並從中提取信息。這就需要一個大團隊，但團隊之間會存在協調配合的問題。範·霍坦博士因此想知道用電腦去做這些繁重的任務是否會有所幫助。
The answer is that it does. His study on the matter, published this week in Patterns, tapped into a branch of machine learning called natural-language processing. This is a way of analysing large volumes of text with minimal human supervision. He and his colleagues identified five existing natural-language-processing systems and borrowed them. They used them to search the abstracts of 4,313 papers on species-conservation projects published over the course of the past four decades. The software’s task was to look for words associated with success, such as 「protect」, 「support」, 「help」, 「benefit」 and 「growth」, and also words associated with failure, like 「threaten」, 「loss」, 「kill」, 「problem」 and 「risk」. Different words had different values attached to them, depending on how positive or negative they were felt to be by the original model-makers. The result was that each abstract could be assigned a sentiment score, averaged from the five different inputs.
答案是肯定的。他對這個問題的研究成果已經發表在本周的《模式》雜誌上，涉及到機器學習的一個分支，即自然語言處理。那是一種只需要少量的人工管理就能完成大量分析文字工作的方法。他和他的同事借用了他們認可的現有五種自然語言處理系統。他們用這五種系統搜尋了過去四十年來已經發表的關於物種保護這一領域的4313篇論文的摘要。這些軟體的任務是去查找關於成功的詞彙，例如：保護，支持，幫助，好處，成長，也查找關於失敗的詞彙，例如：威脅，損失，死亡，問題和風險。不同的詞彙有不同的意義，這取決於最初的模型建造者對它們的評價是積極還是消極。結論是，每篇摘要都可以被標記一個情緒評分，取自五種不同輸入值的平均值。
In total, the team analysed 1,030,558 words. They found that in papers published in the 1980s, when conservation science was in its infancy, terms from the negative list were much more common than those from the positive one. During the past decade, by contrast, terms associated with success became more frequent. Average sentiment scores increased during the study period by 140%.
這個團隊總共分析了1,030,558個詞彙。他們發現，在物種保護科學剛出現的1980年代發表的論文中，負面列表中的詞彙比正面列表的更常見。在過去這十年恰恰相反，關於成功的詞彙出現地更加頻繁。它的平均情緒分數在研究期間增長了140%。
That is clearly encouraging news for conservationists. It suggests that their methods are working in general, and are improving with experience. But more detailed analysis was also possible. Giant pandas, which numbered 1,864 when censused in 2014 and had their status upgraded from 「endangered」 to merely 「vulnerable」 in September, have seen the sentiment of the literature about them swing from negative to positive in a matching way. Papers on the California condor (pictured), by contrast, remain littered with negative sentiments even though its numbers have risen, according to a census in 2016, from an extinction-threatening 22 to 446. But only 276 of those birds were wild, and so the condor is still listed as 「critically endangered」.
這對自然資源保護主義者來說無疑是一個鼓舞人心的消息。這表明，他們的方法總體上是有效的，並且隨著經驗的增加而不斷改進。但是更詳細的分析有不同結論也是合理的。2014年動物數量普查時，大熊貓總共只relevent有1864隻，屬於瀕危動物，但今年九月，大熊貓僅僅被列為了弱勢動物，同時，他們發現有關大熊貓論文的情緒已同步由消極轉變為積極。相反的，圖片上所示的加州禿鷹，在2016年動物數量普查時，數量雖由22增加到446，但作者們還是充滿消極情緒。數量有增加，但是其中只有276隻是野生生物，所以禿鷹還是一直被列為嚴重瀕危動物。
Given the numbers involved, it might be argued that both of these results were predictable. They seem, nevertheless, to be evidence that the method works. And that may be relevant in the context of the team’s analysis of rat-clearance projects intended to help species which have evolved in the absence of those subsequently introduced rodents, and also projects on small islands intended to protect populations of such autochthonous species (often these projects are the same thing). Sentiment analysis sees no clear signal of success here.
考慮到設計的數字，可能會爭辯說這兩種結果都是可預測的。然而，這結果似乎就是這種分析方法有效的證據。這可能在滅鼠項目的團隊分析裡有重要意義，這些項目旨在幫助沒有隨後引入的齧齒動物的情況下進化的物種，以及在小島上開展的旨在幫助此類自生物種的種群數量(通常這些項目是一回事)。情感分析認為這些項目裡沒有明確的成功信號。
Whether Dr Van Houtan’s method might be generalised to other fields of science is debatable. Conservation is, at bottom, an emotion-driven activity. People care about the results in a way that goes beyond professional amour propre. That researchers』 sentiments show up in their choice of words is therefore little surprise, and might well not be true elsewhere. But the fact that Dr Van Houtan has been able to use natural-language processing to expand the pool of papers which can be taken into a review from the hundreds to the thousands suggests that others might benefit from having a look at his achievement.
Van Houtan博士的方法是否可以推廣到其它學科領域還有待商榷。從本質上來說，保護是一種由情感驅動的活動。人們以超越專業愛好的方式來關心結果。因此，研究人員的情感表現在他們所使用的詞彙上也就不足為奇了，在其他領域可能就並非如此了。但是，Van Houtan博士已經能夠使用自然語言處理技術來擴大成被成百上千的人所評論的論文庫的事實，這表明其他的人可能會受益於他的這一成果。
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