NoLimit Analyzes the Conversation on Indonesian Social Media
Lots of people are interested in the Indonesian market and want to know what Indonesians are talking about on social media. But the language can be a barrier, and analyzing and reporting on social media sentiment is difficult and time consuming. The NoLimit team hopes to change that.
NoLimit aggregates all content from Indonesia’s major social networks, analyzes it, and provides easy-to-understand but remarkably deep reports on everything from total conversation volume to discussion sentiment. Supposedly, the algorithm it uses is quite advanced and the team spent more than 11 months making sure it accounts for the intricacies of Indonesian internet slang and other kinds of creative language.
The resulting demo was impressive. A report on political parties in Indonesia showed which parties were dominiating the conversation, and which are the most liked and hated. Data was available over time or on a day by day basis, and goes quite deep — so deep, in fact, that it’s possible to view which specific users are posting the most about a company, how much of what they say is positive or negative, and more.
The market for a service like this is not small. There are more than half a million SMEs in Indonesia, and thousands more government organs, NGOs, international companies, and public figures who might be interested in tracking what’s being said about them in Indonesian social media.
このようなサービスを提供する市場は小さくはない。インドネシアには、50万社以上もの中小企業が存在し、それより数千社以上もの政府関連機関、NGO、海外企業、そして要人などが存在しており、インドネシアのソーシャルメディア上で、一体皆が彼らの何について語っているのかを知りたがっているのだ。
このようなサービスの市場は小規模ではない。インドネシアには50万以上のSMEが存在するといわれ、何千もの政府機関、NGO、国際企業、有名人が自分たちのことがインドネシアのソーシャルメディアではどういうふうに語られているのか調べてみたいと思っている。
This pitch got particularly loud cheers, and the judges seemed pretty happy with it, too. “Sounds legit,” said judge Benjamin Joffe before asking a few clarifying questions. Judge Daniel Saito suggested it ought to be taken global, but NoLimit stressed that its language algorithm is very finely tuned to Indonesian. Saito added that the team needs to learn to explain its algorithm a bit better. Benjamin Joffe also suggested they add correction analysis — some kind of recommendation on how companies can react when they find online sentiment is negative.
This is a part of our coverage of Startup Asia Jakarta 2012, our startup event running on June 8 and 9. For the rest of our Startup Arena pitches, see here. You can follow along on Twitter at @startupasia, on our Facebook page, on Google Plus, or via RSS.
