A new mining and decoding framework to predict expression of opinion on social media emoji’s using machine learning models - Madderi Sivalingam, Saravanan; Ponnaiyan Sarojam, Smitha; Subramanian, Malathi; Thulasingam, Kalachelvi (IAES International Journal of Artificial Intelligence (IJ-AI), 2024)
This research work proposes a new framework mining and decoding (MindE) to predict the expression of opinion on social media emojis using machine learning (ML) models. Expression of opinion can be predicted with short messages on social media. This study used two groups of ML algorithms, convolutional neural network (CNN) ImageNet and CNN AlexNet classifier, and finally, applied the decision tree classifier to predict the type of expression. A recent dataset was taken from Kaggle, an open-source dataset consisting of 7476 rows of emojis for expression of opinion prediction. Accuracy was computed with a G power of 80%, and the experiment was repeated 20 times using both models. After the introduction of the proposed MindE framework, the performance of an expression of opinion prediction will be analyzed with accuracy level. The CNN ImageNet achieved an impressive 97.32% accuracy, whereas the CNN AlexNet algorithm reached only 85.98%. The independent sample T Test indicated a p-value of 0.001, which is below the significance level of 0.05. This suggests that the performance difference between the two ML algorithms is statistically significant. Consequently, the results strongly support the proposed framework “MindE” to predict the expression of opinion on social media emojis.