USE OF NEURAL NETWORKS FOR EMOTION AND OPINION MINING OF SPATIO-TEMPORAL DATA

Human-computer interaction (HCI) relies heavily on emotion detection technology. Due to its portability and ease of use, electroencephalography (EEG) is often used for the estimation of human mood. The recognition accuracy of deep neural network (DNN) methods using an EEG has lately demonstrated amazing improvement. Effective human-robot interaction (HRI) relies heavily on the detection of human emotions. Consequently, there has been a lot of work put into developing reliable and accurate brain-computer interfacing models based on a wide range of biosignals. In instance, studies have demonstrated that an Electroencephalogram (EEG) may provide significant information about a person's emotional state. Comparing the experimental findings utilizing the proposed BoHDF-based to those of previously published research using comparable settings reveals an improvement in performance