Abstract: Embodiments relate to a system, program product, and method for use with an intelligent computer platform and cognitive processing. The embodiments support a mechanism for employing machine learning into dynamically processing a file. File data points are converted to vector form and augmented with similar or synonymous terms. The vector representations of the data points are utilized to identify words and their frequencies, and the identified frequencies are extrapolated and applied to the synonymous terms. As communications are received, the ML model converts the communication to vector form, and compares a feature vector of the communication with data point or file feature vectors to identify and map the communication to a similarly classified file.

Abstract: Provided are embodiments for a computer-implemented method for using social media content to influence driving behavior. The computer-implemented method includes determining, by a processing engine, a user driving behavior, analyzing a social media content of the user, and correlating the user driving behavior and the social media content analysis. The computer-implemented method also includes determining a current user state, and generating a recommendation based at least in part on the current user state and the correlation between the user driving behavior and the social media content. Also provided are embodiments for a computer program product and system for using social media content to influence driving behavior.

Abstract: Embodiments relate to a system, program product, and method for use with an intelligent computer platform and cognitive processing. The embodiments support a mechanism for employing machine learning into dynamically processing a file. File data points are converted to vector form and augmented with similar or synonymous terms. The vector representations of the data points are utilized to identify words and their frequencies, and the identified frequencies are extrapolated and applied to the synonymous terms. As communications are received, the ML model converts the communication to vector form, and compares a feature vector of the communication with data point or file feature vectors to identify and map the communication to a similarly classified file.