3/15/2023 0 Comments Books ngram viewer![]() We might want to see, say, bigrams containing scandal like ‘political scandal’ and ‘religious scandal’ to observe when certain types of scandals come into prominence. It appears like something fairly dramatic happened around 1660 that caused a massive spike in the usage of ‘scandal.’ This in itself could be significant, but we might be interested in more nuanced readings of this data. Take this NGram for the token ‘scandal’ in an English corpus: And we can do the same thing in the NGram Viewer. These are just fancy ways to describe different ways of chunking up a piece of text so that we can work with it. Or, we could use shorthand: we have 3 unigrams or tokens, 2 bigrams, and 1 trigram. ![]() We have three n-grams of length 1 (“a”, “test” and “sentence”), two n-grams of length 2 (“a test” and “test sentence”), and 1 n-gram of length 3 (“a test sentence”). An n-gram is another name for a sequence of words of length n. The above search only accounts for single words, but there are more nuanced ways of using the NGram Viewer. MethodologyĮven with a perfect corpus, our choices can make a big difference in the results we produce. For now, just remember that graphs can appear to express fact when, in fact, the data is murky, subject for debate, or skewed. We can’t know direct truths through the viewer, but we can still use the data for analysis. There were far fewer books published before then, and even fewer are on Google Books.Īs Ted Underwood suggests, when approached with a healthy sense of skepticism, many of these issues do not discount the use of the tool for “relative comparisons between words and periods” after 1820 or so. In addition, the results are better after 1820. The computer can’t infer, for example, that the mispelling ‘scyience’ should be lumped in with the results for ‘science.’ Any underlying problems in scanning or uploading texts will skew the results. The Google Books corpus has also, at times, been criticized for its heavy reliance on poor quality scans of texts to generate their data (more on this in later chapters). A book that only sells one copy is weighted the same as a book that sells a thousand copies: they are both a single copy according to Google’s methods. So things do not get scaled for circulation or popularity. Danforth, and Peter Sheridan Dodds have noted, the corpus only has one copy of each book in its dataset. The corpora for these options are pulled from the Google Books scanning project (to see similar visualizations of your own corpus, you could try working with Bookworm, a related tool). Our results would look a lot different depending on which corpus we selected. The Google NGram Viewer offers a dropdown menu where you can select a corpus to study. What is the corpus, or set of texts, being used to generate this data? The data we choose for a study can skew our conclusions, and it is important for us to think carefully about their selection as a part of the process. While these are fairly stark examples, the same principle holds true: the input affects the output. It would probably look quite different! The same would hold true if we targeted only biology, botany, and physics textbooks over the same time period. Imagine running the same word search for ‘science’ and ‘religion’ over 1000 texts used in religious schools or services. With any large-scale text analysis like this, the underlying data is everything. But not so fast: what is actually being measured here? We need to ask questions about a number of pieces of this argument, including ones regarding: The steady increase of usage of the word science over the last 200 years accompanied by the precipitous decline of the word religion beginning in the mid-nineteenth century could provide concrete evidence for what might otherwise be anecdotal. Looking at the graph, one could see evidence for an argument about the increasing secularization of society in the last two centuries. If we search on ‘science’ and ‘religion,’ for example, we could draw conclusions about their relative importance at various points in last few centuries. The tool allows you to search hundreds of thousands of texts quickly and, by tracking a few words or phrases, draw inferences about cultural and historical shifts. You can specify a number of years as well as a particular Google Books corpus. Provide a word or comma-separated phrase, and the NGram viewer will graph how often these search terms occur over a given corpus for a given number of years. ![]() The Google NGram Viewer provides a quick and easy way to explore changes in language over the course of many years in many texts. The Google NGram Viewer is often the first thing brought out when people discuss large-scale textual analysis, and it serves nicely as a basic introduction into the possibilities of computer-assisted reading. ![]()
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