An effective method of improving accuracy in temperament identification using text messages and social media history with gated recurrent unit algorithm in comparison with random forest algorithm

This research proposes an effective method for improving accuracy in identifying temperament history and text messages and comparing the performance of algorithms. Iterations were conducted during the research for a total sample size of 3062. The csv file repositories on the Kaggle website provided the data set for the study. Which is carried out by setting the G-power as 0.80, with the alpha computation as 0.05 and beta computation as 0.2, with the Confidence range of 95.0%. An accuracy of 93.40% and a loss of 6.59% were achieved with the implementation of the GRU model, which outperformed the Random Forest model with its accuracy of 81.52% and loss of 13.33%. The analysis uncovered a substantial difference among the 2 groups, as evidenced by a calculated p-value of 0.001.GRU algorithm with 93.40% outperforms the RF algorithm with 81.52% in terms of accuracy.