Case Study: Hootsuite improves social media language detection with Babel Street Text Analytics

Rosette Delivers Accurate and Fast Language Detection for Social Media Monitoring

Hootsuite, the social media management platform used by millions of people, needed a better way to segment social media messages by language so posts could be routed to the right regional teams and customer support reps. Its home-grown language detector became costly to maintain as new expressions emerged over time, so the company turned to Babel Street Text Analytics for a more reliable approach.

Babel Street Text Analytics provided fast language detection based on statistical language profiles and a short-string algorithm designed for tweets, search queries, and captions. With Babel Street, Hootsuite can accurately tag posts by language, helping customers estimate staffing needs across languages and direct messages to the right person for response. The result was strong enough that Hootsuite said it rarely revisits the decision, and the improved precision lets the team focus on other priorities.

Hootsuite

Mihai Caraman

Development Manager