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Enterprises and investors are increasing their use of natural language APIs to assist processing in tasks like data mining for sales intelligence, tracking how marketing campaigns change over time, and better defending against phishing and ransomware attacks.

Still, AI products that use natural language engines to analyze text have a long way to go to capture more than a fraction of the nuance humans use to communicate with each other. hopes the addition of new emotion- and behavior-measuring extensions and a new style-detecting toolkit for its natural language API will provide AI developers with more humanlike language analysis capabilities. The company this week announced new advanced features for its cloud-based natural language API designed to help AI developers “[extract] emotions in large-scale texts and [identify] stylometric data driving a complete fingerprint of content,” said in a statement.

Based in Modena, Italy and with U.S. headquarters in Rockville, Maryland, changed its name from Expert System in 2020. The company’s customers include media outlets like the Associated Press, which uses NL software for content classification and enrichment; business intelligence consultants like L’Argus de la Presse, which conducts brand reputation analysis with NL processing; and financial services firms like Zurich Insurance, which uses’s platform to develop cognitive computing solutions.

Freeing people up for higher-order tasks’s software platform enables natural language solutions that take unstructured language data from sources like social media sites and emails, transforming it into more digestible, usable intelligence before human analysts look at it. An example of a basic NL capability would be to distinguish between different ways a word like “jaguar” is used contextually — to signify the animal, the vehicle, or the name of a sports team. This allows for process automation steps to be introduced to text gathering, categorization, and analysis workloads, which frees up human analysts to perform higher-order tasks with the data.


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Several NL software developers, including, used algorithms last year to attempt to predict the outcome of the U.S. presidential election, with mixed results. While trying to weed out bot accounts, scraped Twitter and other social media sites to determine which candidate was ahead on “positive” sentiment and thus likely to win the popular vote. The company’s final polling gave Joe Biden a 50.2% to 47.3% edge over Donald Trump — not too far off Biden’s final tally of 51.3% to Trump’s 46.9% of the national popular vote.

With the new extensions, the natural language API now captures a range of 117 different traits in analyzed language, the company said. The natural language engine categorizes eight different “emotional traits” found in analyzed text (anger, fear, disgust, sadness, happiness, joy, nostalgia, and shame) and seven different “behavioral traits” (sociality, action, openness, consciousness, ethics, indulgence, and capability). Traits are further rated on a three-point scale as “low,” “fair,” or “high.”

Natural language API identifies individual authors

Additionally,’s new “writeprint” extension improves the NL engine’s ability to process and understand the mechanics and styles of written language. The writeprint extension “performs a deep linguistic style analysis (or stylometric analysis) ranging from document readability and vocabulary richness to verb types and tenses, registers, sentence structure and grammar,” according to the website. The ability to identify individual authors of texts via the writeprint extension could be put to several uses, such as identifying forgeries or impersonations, as well as categorizing content based on writing style and readability, the company said.

“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,” head of product management Luisa Herrmann-Nowosielski said in a statement.

“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,” she added.

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