Inaccuracy in the Expression of Embodied Rationale Based on Emotional Tweets Use of the Novel K-Modes Algorithm is Suggestion Convolutional Neural Network Comparison Algorithm

Abstract

Aim:  The main objective of this study is to use machine learning classifiers to improve the accuracy percentage of recognising relevant people with distinct emotions in order to promote emotive tweets. Novel K-Modes algorithm and the CNN (Convolutional Neural Network) algorithm. Materials and Methods: Novel K-Modes object detection algorithm with a sample size = 132 and the Convolutional Neural Network algorithm with a sample size = 132 were tested several times in predicting the accuracy percentage with a confidence interval of 90% and G Power (valu= 0.6). K-Modes use the weights and configurations of the prediction to overlay it. Result: The K-Modes algorithm has improved accuracy (72.60%) when compared to Convolutional Neural Network accuracy (62.80%). The results achieved with significance value p=0.259 (p>0.05) shows that two groups are statistically insignificant.Conclusion: The accuracy of the K-Modes method was significantly better than that of the Convolutional Neural Network technique.