Abstract: Disclosed embodiments may include a method for generic aspect-based sentiment analysis. The system may receive training data, which is used to train one or more machine learning models. The system may receive data, which may be transcribed call data. The system may extract one or more aspects from the call data using one machine learning model. For each aspect, the system may determine a sentiment polarity. The system may filter the aspects and sentiment polarities and analyze trends based on the filtered aspects and sentiment polarities. The system may output a result to a dynamic graphical user interface based on the trends. This may allow a user to detect customer attitudes toward a variety of subjects and trends over time without training machine learning models for specific domains.

Abstract: A system and method of operating a system for automatically punctuating text using non-recurrent neural networks is disclosed. The system and method at least: applying a text string to a first component of a non-recurrent neural network trained to generate one or more contextualized vectors, wherein the first component determines the contextualized vectors by processing each word in the text string in parallel with one another; applying the contextualized vectors to a second component of the non-recurrent neural network trained to generate a set of probability values for each word in the text string, wherein the second component determines the set of probability values by processing the contextualized vectors in parallel with one another; and transmitting the set of probability values to a text generation engine to generate a formatted text string based on the set of probability values.

Abstract: Disclosed herein are system, method, and computer program product embodiments for machine learning systems to process incoming call-center calls based on inferred sentiments. An incoming call is routed to a call agent based on an inferred topic, classified based on one or more inferred sentiments of a current caller's speech, determining, based on the call classification, that a complaint has been articulated and initiating an automated assistance by searching for one or more similar callers to the current caller. Based on finding a successful call outcome associated with one or more similar callers, the system suggests one or more phrases to the call agent for use in a dialog with the current caller to improve the one or more inferred sentiments.

Abstract: Disclosed herein are system, method, and computer program product embodiments for machine learning systems to process incoming call-center calls based on inferred sentiments. An incoming call is routed to a call agent based on an inferred topic, classified based on one or more inferred sentiments of a current caller's speech, determining, based on the call classification, that a complaint has been articulated and initiating an automated assistance by searching for one or more similar callers to the current caller. Based on finding a successful call outcome associated with one or more similar callers, the system suggests one or more phrases to the call agent for use in a dialog with the current caller to improve the one or more inferred sentiments.

Abstract: A system and method of operating a system for automatically punctuating text using non-recurrent neural networks is disclosed. The system and method at least: applying a text string to a first component of a non-recurrent neural network trained to generate one or more contextualized vectors, wherein the first component determines the contextualized vectors by processing each word in the text string in parallel with one another; applying the contextualized vectors to a second component of the non-recurrent neural network trained to generate a set of probability values for each word in the text string, wherein the second component determines the set of probability values by processing the contextualized vectors in parallel with one another; and transmitting the set of probability values to a text generation engine to generate a formatted text string based on the set of probability values.