Brexit on a Global Scale
Do you have a truly global perspective on the aftermath of the UK’s Brexit?
In the event you missed it, the United Kingdom shocked the world early this morning when they elected to be the first country to leave the European Union.
The decision will begin a protracted process of re-negotiating trade, business, and political ties between the UK and the newly shrunk EU, a breakup that could take years to fully formalize.
Global interest, anglo coverage
There’s an enormous amount of chatter surrounding ‘Brexit’ now that the poll results are counted. Here are some snapshots from around the world using google trends showing Bexit as a top, courtesy of Google Trends:
Despite this overabundance of news articles, tweets, and the like responding to the UK’s decision, getting a global read on Brexit is hard! Google’s “Full Coverage” returns a very anglo-centric view, pulling up only English articles published by English-language news sources.
The only real way to get a realistic global read on the Brexit story is to collect local and untranslated feeds from around the world and base your exploration on that “clean” data, but that typically takes a prohibitive investment of manpower and some serious language skills.
“GlobalPost, for example, with money from investors, Web advertising, and fee-paying clients, produces independent foreign reporting with a string of sixty-five professional stringers. On the home front, Politico has a news staff of seventy, and delivers scoops, gossip, and commentary on national politics and government.” – Leonard Downie Jr. and Michael Schudson in their Columbia Journalism Review report on “The Reconstruction of American Journalism.”
A good eurocentric article on the Brexit can be found in Le Figaro, but it’s in French. Basic translation services such as Google Translate do a mediocre job getting at the finer points, and you still have to then digest and extract the key information. The most thorough comprehension stems from running analysis in the native language, with tools created for that language.
With the enormous economic, political, and social turbulence that is already following (and will continue to follow) from this unprecedented move, being able to encompass global perspectives is vital to any organization, both in the aftermath of the Brexit, and in preparation for whatever major global event comes next.
So what do you need to consider when mining the web for news?
Skillful mining of news data has become a critical component of maintaining a competitive business advantage. Going beyond sentiment analysis, companies increasingly seek predictive insights into the influences, thoughts, emotions, and ideas that drive consumer buying decisions. This involves a number of challenges, including:
- Analysis of social media data crosses geography, language, and culture
- The exponentially growing number of social networks users doubling the worldwide quantity of digital data every year
- The need to include tools for mining images, audio, video, and emoji
Ask yourself: does your solution comprehensively monitor news and social data to track information that affects your bottom line? Is it able to evaluate data sources from different countries?
The essential key to all of it—analyzing customer experiences, market research, consumer insights, digital analytics, and media measurement—is high-quality, multilingual Text Analytics. Unlike years past when machine translation was the only option, today’s cutting edge tools apply linguistic analysis to structured and unstructured text (news, reviews, blogs, tweets, and posts), so each word is understood in its native context. It’s the only way to deliver results that are not skewed by subtle errors in slang, syntax, or spelling.
The tools your customers need to successfully mine any data feed exist. And in an increasingly crowded marketplace your software must:
- Provide high quality results across ALL the languages your customers want
- Offer robust features, functionality, and scaling
- Integrate easily into existing infrastructure
Your solution should also meet both your customers’ current needs and position them well for future growth.
Essential Questions to Ask About Your Text Analysis Capabilities
| How does your text analytics handle short text, like Tweets? | Tweets have traditionally been more difficult to analyze because there is less context to work from, and they often include slang, abbreviations, and emoticons. But many solutions are now capable of excellent Twitter analysis—identifying the language and finding mentions of people, places, and companies. |
| Can my system analyze text in industries where jargon and specialized vocabulary exists? | If your customers use domain-specific vocabulary, they need analytics that can be trained for greater accuracy over time. So, look for text analytics that work right out of the box, but also can adapt to meet the specific and evolving requirements of any domain. |
| How do I guarantee high quality results across all languages? | Any analytics you build is only as good as the linguistic analysis foundation it’s based on. Machine translation is old school. You need a solution where each language is understood natively. This linguistic approach does not find related words based on how they appear but rather, what each word means within its written context. It’s the best way currently available to ensure all data is interpreted correctly. This is also a reason you may prefer a company whose core competency is text analytics rather than one where analyzing Big Data is just one aspect of a wider suite of products. |
| How many languages should my textual web mining solution accommodate? | To best position your company for future growth while minimizing both integration headaches and time-to-market, the short answer is as many as you are likely to need. But rather than adding languages piecemeal over time, you may be better served by a vendor known for the quality of its multilingual capability, across many languages. |
| How well does your product respond to the idiosyncrasies of search in different languages? | Comprehensive and reliable search results depend on native understanding of each language. Minor variations in spelling (color vs. colour) and characters (Tschüß vs. Tschuess) exist in all languages . The complexities of Chinese, Arabic, and Japanese pose greater challenges. Can your search engine accommodate all these variations? |
| How well does your product track names in multiple languages? | Because many brands are now recognized internationally, multilingual capability has become critical. |
“Cheerio, United Kingdom”
While it will certainly take some time for markets, currencies, and heart rates to stabilize after the UK Brexit, we hope both parties can take a leaf out of recently split pop icons Calvin Harris and Taylor Swift’s positivity:
If not, the remaining EU members can at least comfort themselves singing along to one of our century’s top breakup anthems, and trying to determine which Pitch Perfect character best represents their country. Cheers!