In a world where global barriers have been steeply reduced, understanding the diverse languages your audience speaks is a top priority. Relative Insight is excited to announce the addition of its new Spanish language functionality, enabling businesses to unlock new opportunities through data-driven decisions.
The new feature empowers users to fully analyze and understand Spanish-language text data, using topical, grammar, and emotional analysis with native precision to reach customers across global markets.
Discovering insights with native precision
Rather than deploying a third-party translation tool, we have created our own framework, allowing users to gain valuable insights into customer sentiment, brand perception, and competitor analysis. The platform’s natural language processing (NLP) algorithms will enable users to quickly identify trends and emotions in their Spanish data, for an in-depth understanding and a competitive edge in the market.
A lot can get lost in translation… That’s why native Spanish text analysis capabilities allow for a deeper understanding of your data, revealing hidden meaning in a way that translation services can’t.
To do this, we used state-of-the-art NLP models and a proprietary technique to build our topic model (containing 420 different topics). We then tested it with native speakers from several Spanish-speaking countries.
Analyze your Spanish text data
Getting started is easy! Simply select Spanish from the language set drop-down menu when uploading your data. We designed the new feature with ease of use in mind, providing users with a comprehensive text analytics solution.
As the fourth most spoken language in the world, our Spanish language feature is a valuable asset to organizations seeking to expand their reach across global markets.
“We are excited to bring Spanish topical analysis to Relative Insight. This will allow our customers to analyze and understand their language data more effectively,” said Ryan Callihan, Head of AI at Relative Insight.
“Translation to English may suffice for some, but we wanted to capture the nuance that gets lost. We’re dedicated to delivering the best results, so we found a way to get the native precision that makes all the difference.”