Abstract: Provided is a method for detecting deceptive e-commerce reviews based on a sentiment-topic joint probability, which belongs to the fields of natural language processing, data mining and machine learning. In the data of different fields, a STM model is superior to other reference models; compared with other models, the STM model belongs to a completely un-supervised (no label information) statistic learning method and shows great advantages in processing unbalanced large sample dataset. Thus, the STM model is more suitable for application in a real e-commerce environment.

Abstract: The present invention discloses an intelligent traffic safety pre-warning method, a cloud server, onboard-terminals and a system. The method comprises: a step (101): the onboard-terminal establishes a communication connection with the cloud server; a step (102): the onboard-terminal acquires data, and uploads data calculated based on the acquired data to the cloud server; a step (103): the onboard-terminal receives feedbacks from the cloud server, the feedback comprising the probability that the current vehicle has an accident within a set range of the current road segment; and a step (104): the onboard-terminal receives the probability that the current vehicle has an accident within the set range of the current road segment, and then transmits the feedback to the driver by human-computer interaction.

Abstract: Provided is a method for detecting deceptive e-commerce reviews based on a sentiment-topic joint probability, which belongs to the fields of natural language processing, data mining and machine learning. In the data of different fields, a STM model is superior to other reference models; compared with other models, the STM model belongs to a completely un-supervised (no label information) statistic learning method and shows great advantages in processing unbalanced large sample dataset. Thus, the STM model is more suitable for application in a real e-commerce environment.

Abstract: The present invention discloses an intelligent traffic safety pre-warning method, a cloud server, onboard-terminals and a system. The method comprises: a step (101): the onboard-terminal establishes a communication connection with the cloud server; a step (102): the onboard-terminal acquires data, and uploads data calculated based on the acquired data to the cloud server; a step (103): the onboard-terminal receives feedbacks from the cloud server, the feedback comprising the probability that the current vehicle has an accident within a set range of the current road segment; and a step (104): the onboard-terminal receives the probability that the current vehicle has an accident within the set range of the current road segment, and then transmits the feedback to the driver by human-computer interaction.