CASE STUDYRoad to Japan: How to Yelp Like a NativeYelp Inc. is a community-driven platform that connects people with local businesses.
Millions of people rely on Yelp for useful and trusted local business information, reviews,
and photos to help inform their spending decisions. Now in numerous cities around theglobe, consumers turn to Yelp to find businesses in their neighborhood.

The Challenge
When Yelp wanted to expand beyond the U.S.
and Europe, it began to study launching a sitein Japan, another large market with consumers
who are passionate about local businesses
from sushi bars to nail salons, contractors todentists. However, at that point, Yelp had only
dealt with European languages. Asian languages
like Japanese are more challenging since theyoften don’t use spaces between words — andidentifying words is necessary for the searchcapabilities Yelp uses to ensure community
members find businesses across numerous
categories and criteria.

linguistic analysis system that would meet theserequirements:

Group Product Manager of Search and Data
Science at Yelp, Travis B. and his team realized
they needed to create a polished productthat would encourage immediate communitybuilding among Japanese users. The team at Yelp
determined they needed a high-performance

The Solution■ Accurate tokenization (breaking text intowords) and part-of-speech tagging forJapanese■ Linguistic processing that would not degrade
system performance when added to every
search query■ A system that could scale with Yelp’s
international expansion plan and improveoutcomes for other languages beyond Japanese

Two factors led Yelp to choose Babel Street Text
Analytics for linguistic processing:Number of languages supported: Yelp didn’t findany other uniformly high-performing package

babelstreet.com

that offered the same functionality and numberof languages (40+) as Text Analytics. With Text
Analytics’ language coverage, Yelp engineers
would not have to integrate multiple language
packages into the code base.
Product Support: Yelp valued the support
offered by the Text Analytics team, which
would have been unavailable from open-source
linguistics packages.
“These [other] packages had pretty goodquality compared to Text Analytics for certain
languages, but we would have had to stitch them
together, and we wouldn’t have had someone toturn to when there was a problem,” Travis said.
“Text Analytics’ support and guidance was very
helpful. There were a couple of instances earlyon regarding the best way to implement Text
Analytics to optimize performance. Their support
team helped us get Text Analytics performing
the way we wanted. If we had gone with multiple,different packages, it would have been a lot ofdebugging work for our engineering team.”

After adding Text Analytics to Yelp’s search,
Travis reported, “We were able to get our internal
assessment of search quality to a point where
we felt comfortable shipping the product. As wemeasured how users engage with search results,
and from human evaluation, the improvement isdue to fewer misinterpreted search queries.”“If we misunderstand the language, and shipcore functionality that reads as broken in a local
language — it makes us look like a company
that doesn’t understand the local customs andlanguages and makes it hard to build a communityof local users. We rely on local people who are
passionate about their local businesses, so it’sdoubly important to get this right.”

The Result
Yelp Japan has been well-received since it
launched, and has been one of Yelp’s fastest
growing international markets. Since then Yelp,
has used Text Analytics in many languages for
Yelp search and Yelp highlights functionality.

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