Harmony Gradient Boosting Random Forest Machine Learning Algorithms for Sentiment Classification
G. Komarasamy, Subash Chandra Bose Jaganathan, Sridharan. K, Anisha Mital, Sanika Awal · 2022
The exchange of ideas and opinions on social media is increasingly becoming essential. A vast amount of data is produced by the intense analysis of online social media. The harmony random forest machine learning algorithm is suggested in this study as a method for sentiment analysis on social media. Additionally, social media data sentiment analysis employs the gradient boosting harmony algorithm. Compared to other methods, the harmony gradient boosting random forest machine learning technique yields better outcomes. In contrast to the gradient boosting approach, which creates ensembles of weak and shallow succeeding trees, random forest develops ensembles of independent trees. In comparison to the harmonic random forest, the suggested approach has a classification accuracy of 5.68%. Thus, the suggested harmony gradient boosting random forest machine learning is more effective than.