Abstract: Machine learning-based approaches are used to detect trends of behavior and anomalous events from customer support messages between customers and customer support agents or other appropriate resources in an electronic environment. For example, for a plurality of time periods, a prediction model can be trained. The prediction models can be trained on messages that correspond to each prediction models' period of time. The prediction models can process messages to determine a score (e.g., a representative confidence score) for the time period a prediction model is associated with. For a selected time period, a model (e.g., a trend detection model) can be applied to the scores for time periods before the selected time period to determine whether the score for the selected time period is associated with an anomalous event. Thereafter, an alert can be presented with, for example, the messages that triggered the alert, among other such information.

Abstract: Machine learning-based approaches are used to detect trends of behavior and anomalous events from customer support messages between customers and customer support agents or other appropriate resources in an electronic environment. For example, for a plurality of time periods, a prediction model can be trained. The prediction models can be trained on messages that correspond to each prediction models' period of time. The prediction models can process messages to determine a score (e.g., a representative confidence score) for the time period a prediction model is associated with. For a selected time period, a model (e.g., a trend detection model) can be applied to the scores for time periods before the selected time period to determine whether the score for the selected time period is associated with an anomalous event. Thereafter, an alert can be presented with, for example, the messages that triggered the alert, among other such information.