It is tempting to treat frequency trends from the Google Books data sets as
indicators of the "true" popularity of various words and phrases. Doing so
allows us to draw quantitatively strong conclusions about the evolution of
cultural perception of a given topic, such as time or gender. However, the
Google Books corpus suffers from a number of limitations which make it an
obscure mask of cultural popularity. A primary issue is that the corpus is in
effect a library, containing one of each book. A single, prolific author is
thereby able to noticeably insert new phrases into the Google Books lexicon,
whether the author is widely read or not. With this understood, the Google
Books corpus remains an important data set to be considered more lexicon-like
than text-like. Here, we show that a distinct problematic feature arises from
the inclusion of scientific texts, which have become an increasingly
substantive portion of the corpus throughout the 1900s. The result is a surge
of phrases typical to academic articles but less common in general, such as
references to time in the form of citations. We highlight these dynamics by
examining and comparing major contributions to the statistical divergence of
English data sets between decades in the period 1800--2000. We find that only
the English Fiction data set from the second version of the corpus is not
heavily affected by professional texts, in clear contrast to the first version
of the fiction data set and both unfiltered English data sets. Our findings
emphasize the need to fully characterize the dynamics of the Google Books
corpus before using these data sets to draw broad conclusions about cultural
and linguistic evolution.

通过对谷歌图书语料库含有学术性文章的部分进行分析，本文强调了在使用谷歌图书数据集得出有关文化和语言演变的广泛结论之前，有必要充分了解谷歌图书语料库的动态。

谷歌图书语料库的特征化：对社会文化和语言演化推断的强限制

Characterizing the Google Books corpus: Strong limits to inferences of  socio-cultural and linguistic evolution

Today we have access to unprecedented amounts of literary texts. However,
search still relies heavily on key words. In this paper, we show how sentiment
analysis can be used in tandem with effective visualizations to quantify and
track emotions in both individual books and across very large collections. We
introduce the concept of emotion word density, and using the Brothers Grimm
fairy tales as example, we show how collections of text can be organized for
better search. Using the Google Books Corpus we show how to determine an
entity's emotion associations from co-occurring words. Finally, we compare
emotion words in fairy tales and novels, to show that fairy tales have a much
wider range of emotion word densities than novels.

本文探讨了情感分析如何与有效的可视化技术结合使用，定量和跟踪书籍中的情绪及其在大型文集中的表现。本文还介绍了情感词密度的概念，并以格林兄弟童话作为例子，展示了如何更好地组织搜索文本。最后，通过比较童话和小说中的情感词，本文表明童话中的情感词密度要比小说更广泛。