Mature businessman or a scientist with gray hair with a robot/artificial intelligence (AI)
PHOTO: Shutterstock

The natural language (NL) API developed by, an artificial intelligence platform for language understanding, boasts new advancements of features for its analysis capabilities according to a recent press release. Extracting emotions on a large-scale and identifying stylometric — meaning the application of the study of linguistic style — data that propels a complete fingerprint of content remains among the biggest challenges of AI developers within the NL ecosystem.

Leveraging the analysis of emotional and behavioral traits plays a critical role in advancing the development of intelligent NL apps. Providing insight into the state of mind, developers and data scientists can go beyond the surface-level positive or negative ratings of a sentiment analysis to identify the emotions of a text. This is aimed at providing insight that will enable organizations to create applications that apply the full potential of their data.

"From apps that analyze customer interactions, product reviews, emails or chatbot conversations, to content enrichment that increases text analytics accuracy, adding emotional and behavioral traits provides critical information that has significant impact," said Luisa Herrmann-Nowosielski, Head of Product Management at in a press release. "By incorporating this exclusive layer of human-like language understanding and a powerful writeprint extension for authorship analysis into our NL API, we are conquering a new frontier in the artificial intelligence API ecosystem, providing developers and data scientists with unique out-of-the-box information to supercharge their innovative apps."

How is this achieved within the API? To put it plainly, by capturing a range of emotional and behavioral traits. The NL API works to capture a range of 117 different traits, providing an emotional and behavioral taxonomy. Emotional Traits are categorized into 8 groups (anger, fear, disgust, sadness, happiness, joy, nostalgia, shame…), while Behavioral Traits are divided into 7 groups (sociality, action, openness, consciousness, ethics, indulgence and capability). Then, the API assigns 3 levels of polarity (low, fair, high) to further indicate the level of each trait extracted.

The extension can be put to use to make media content categorization more effective and improve customer interactions and enabling more effective recommendation tailoring for e-commerce and online advertising, for example.

The NL API writeprint extension performs a deep linguistic style analysis (a.k.a. “stylometric analysis”) tackling cognitive tasks ranging from identifying document readability and vocabulary versatility, to verb types and tenses, registers, sentence structure and grammar. By comparing multiple documents to identify unique writing style and author invariants to streamline authorship analysis, it can establish the author of a specific text or isolate characteristics such as education level and other cultural aspects.

Writeprint features may prove useful for things like knowing with a degree of certainty, whether a specific person wrote a text and can help identify forgeries or impersonations. They are also useful for categorizing news and articles according to distinct writing styles or the readability level. has created an opportunity to build an AI-based app leveraging the advanced features provided by the NL API by launching a new "Sentiment & Opinion Mining Natural Language API" hackathon. Learn more about the hackathon.