Ensemble Machine Learning Models Based on Predictions for Sentimental Analysis on Twitter Data
G. Rajaramesh, S Shashank, S Sreeniketh Rao, M Prakash
Asian Journal of Research in Computer Science · pp. 450–458 · Published 1 May 2025
10.9734/ajrcos/2025/v18i5666Abstract
In current days, web content comes from social media, multiple companies, different types of events, online products and personal data. This sentiment analysis predicts findings with the help of different methodologies. We used machine learning models for this research. In this process, the input is so simple, but deriving this information is too difficult. Internet data usage is increasing throughout the world, using this data is used for feedback purposes. Such a type of data classification and organize was most difficult for sentiments. This feedback is most important for improving the business, gaining more profit and understanding the customer’s interest. Finally, from our research, Logistic regression accuracy is 92%, XGBoost accuracy is 90%, Decision trees predict 90% accuracy, and Random forests predict 95.5% accuracy. Compared to the ensemble learning model, the Random Forest Tree model achieves a higher accuracy rate than the ensemble models.
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