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Organizational culture is an evergreen topic for business books, websites, articles, position papers, blogs, tweets, Likes, +1’s, corporate retreats, conference tracks and keynote addresses (and a few more places). At the heart of that culture are a company’s core values – what “will never change” as Jim Collins points out.
Check out W2O Group President Bob Pearson's new book, Pre-Commerce, in which he shares ideas for leaders to engage directly with customers to shape their brand and marketplace success. Linguistic Mapping Reveals How Word Meanings Sometimes Change Overnight Data mining the way we use words is revealing the linguistic earthquakes that constantly change our language. A similar change occurred to the word “mouse” in the early 1970s when it gained the new meaning of “computer input device”. All this serves to show how language constantly evolves, often slowly but at other times almost overnight. Today, Vivek Kulkarni at Stony Brook University in New York and a few pals show how they have tracked these linguistic changes by mining the corpus of words stored in databases such as Google Books, movie reviews from Amazon and of course the microblogging site Twitter. Statistically Significant Detection of Linguistic Change by Vivek Kulkarni, Rami Al-Rfou, Bryan Perozzi, and Steven Skiena. We propose a new computational approach for tracking and detecting statistically significant linguistic shifts in the meaning and usage of words.
We consider and analyze three approaches of increasing complexity to generate such linguistic property time series, the culmination of which uses distributional characteristics inferred from word co-occurrences. We demonstrate that our approach is scalable by tracking linguistic change across years of micro-blogging using Twitter, a decade of product reviews using a corpus of movie reviews from Amazon, and a century of written books using the Google Book-ngrams.
While the authors are concerned with scaling, I would think detecting cracks, crevasses, and minor tremors in the meaning and usage of words, say between a bank and its regulators, or stock traders and the SEC, would be equally important.
Detecting earthquakes in meaning is a worthy endeavor but there is more tripping on cracks than falling from earthquakes, linguistically speaking. Incorrect system language settings can play havoc with your smartphone and Word documents, so follow these steps for switching to UK English. Fortunately, it’s relatively simple to fix this, although you may face problems if your smartphone is set to something other than English, since the whole interface will appear in a foreign language.
Although these steps show how to change language settings to English (UK), you can obviously use them to change to any language you like.


Android has a different appearance on almost every smartphone it’s used on, so these screenshots and the exact wording for the LG G3 may not match your smartphone exactly. Find the Languages and input option (this may be under the General or Personal section) and select it. If English (United Kingdom) isn’t shown under Language, tap this option and select English (United Kingdom) from the list of language options.
When the Word Options dialog box opens, select Popular from the list of options at the left and click the Language Settings button. If you don’t see English (United Kingdom) in the list of Enable editing languages, select it from the list on the left and click the Add button. If you don’t see English (United Kingdom) in the list below Choose Editing Languages, select it from the drop-down list and click the Add button. Unfortunately, these values can end up marginalized in marketing and communications, thought of as just another concept to shoehorn into messaging. In this light, differences between Japanese and English can be viewed similarly to the differences between the internal languages of Google and SAS (ranked 1 and 2 respectively on FORTUNE’s 100 Best Companies to Work For). This doesn’t mean your company lacks integrity, only that you may need to find creative ways to illustrate that moral footing. The first is a simple count of how often words are used, using tools such as Google Trends. Such linguistic shifts are especially prevalent on the Internet, where the rapid exchange of ideas can quickly change a word’s meaning.
Using recently proposed deep neural language models, we first train vector representations of words for each time period. Our analysis reveals interesting patterns of language usage change commensurate with each medium. Smartphones, tablets and even individual applications like Microsoft Word can think you’re fluent in English (US) rather than English (UK), which can be caused by a quirk in how they were first set up or by simply tapping something in their settings buy mistake. The steps should be close enough for you to follow for your Android smartphone or tablet, though.
Word will ask you to confirm you want to change the default language - click the Yes button.


If this isn’t set to English (United Kingdom), select that option from the drop-down list and click the OK button, then click OK again to close the Word Options dialog box. A company’s culture can affect its performance, from recruitment and employee retention to innovation and market perception. So imagine a company’s power to guide employee, partner and customer perception, and achieve greater goals around employee recruitment, satisfaction and innovation, simply by choosing and reinforcing the right language. Instead of discovering what the company stands for – what they believe will never change – you’d find the limited items Google believes are not true, exposing a whole world of questions on what it believes is true. Stories of core values in practice are also great ways to highlight employees and the company’s valued behaviors.
Just months before, the word “sandy” was an adjective meaning “covered in or consisting mostly of sand” or “having light yellowish brown colour”.
And later, the word “windows” followed a similar course after the release of the Microsoft operating system. For example, in October 2012, the frequency of the words “Sandy” and “hurricane” both spiked in the run-up to the storm. Our meta-analysis approach constructs property time series of word usage, and then uses statistically sound change point detection algorithms to identify significant linguistic shifts. But what if I told you this language has the power to change behavior and shape the way your employees (and customers) see the world, your company or their jobs?
Almost overnight, this word gained an additional meaning as a proper noun for one of the costliest storms in US history. However, only one of these words changed its meaning, something that a frequency count cannot spot.
Finally, we construct a distance-based distributional time series for each word to track it’s linguistic displacement over time.



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