Text Mining Exercise with Google NGram

In this exercise, we shall walk through an example of text mining in the form of Googles Ngram Viewer. Google maintains a large repository of optical character recognized (scanned) books in its library known as Google Books. The Google Ngram Viewer is an offshoot of this project and is a simple, free text mining tool allowing researchers to search, compare and visualize information from its extensive collection of published documents (Google Books), in the form of n-grams. This allows users to assess cultural and human-interest trends across a long period (1500 to 2019, or any amount of years between those dates). This covers approximately 4% of all books ever published, or in other words about 5.2 million books; the largest collection of published texts anywhere that has been made available for mining. Google Ngram Viewer allows you to rapidly quantify trends of various sorts over some time including:

o People

o Events

o Terms

o Grammar

o Spelling

The Ngram Viewer allows you to follow trends in these topics but also might reveal oddities in the results caused by censorship, the invention of new technology, or events that are occurring around those times (An introduction to text mining:

2. Case study: Ngram Viewer, 2019) It also offers the option for breaking up English usage between UK and US publications. Note that the Ngram viewer is case-sensitive. Exercise: Try it Yourself Now it is your turn to have a go. Lets first use an example of how the description of the term analytics has changed over the last two centuries. The most common categories are 'Analytics', Data Science and Data Analytics. How does the usage of those terms change over time?

A. 1. Open up the Ngram Viewer in another browser window (click here - https://books.google.com/ngrams)

2. In the phrases box type in your search terms with a comma separating each item : Data Analytics, Data Science

3. Choose the date range 1800-2019 (this is the default setting) and the default setting of English(2019)

4. Choose a smoothing rating (we chose 2 to begin)

5. Hit Enter on your Keyboard What do you see? You should get a line graph where both terms gained significant mention in publications after 2010. If you have a screen snipping tool, you may insert your screenshot below:

B. Now include the term 'Analytics' into the mix: Type in: Data Analytics, Data Science, Analytics Describe what you observe. If you have a screen snipping tool, you may insert your screenshot below:

C. Now compare with lower case instances of the same terms, with the same settings: Data Analytics, Data Science, Analytics, data analytics, data science, analytics What do you observe? What could explain the difference in trends between the way these terms are expressed? Note your observations below. PS: Feel free to try your combination of terms. For example, try comparing capital cities of the world Washington, London, Paris, Berlin, Delhi, Tokyo, Buenos Aires To learn more about Google Ngram Viewers usage and its limitations, go to https://port.sas.ac.uk/mod/book/view.php?id=554&chapterid=328 Reference: An introduction to text mining:

2. Case study: Ngram Viewer. (n.d.). Port.sas.ac.uk. Retrieved November 8, 2021, from https://port.sas.ac.uk/mod/book/view.php?id=554&chapterid=327