Improved Accuracy for Exploring Text - Based Emotion Recognition in Social Media Conversation Generalized Linear Model Compared with Random Forest
K. Likhitha, K. Sashi Rekha, S. Ramesh · 2023
Aim: The primary goal of this research was to evaluate the efficacy of the New Generalized Linear Model (GLM) and the Random Forest Algorithm in identifying the sentiment of social media posts. Materials and Methods: We estimate numerous times using the Generalized Linear Model with a sample size of 10, and using the Random Forest with a sample size of 10, to predict with an accuracy of 93.01%. Results: In this study, the accuracy of the Generalized Linear Model (GLM) Algorithm was found to be 70%, which is significantly higher than the accuracy of the Random Forest Algorithm (85.18%). With a pre-test probability of 80%, p=0.824 (p0.005) is not statistically significant. Conclusion: In summary, the Generalized Linear Model (GLM) outperformed the Random Forest algorithm when it came to examining text-based emotion recognition in social media interaction.