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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. In this process, input is so simple but deriving this information is too difficult. Internet data usage is increasing worldwide, using this data for feedback purposes. Such type of data classification and organization was most difficult for sentiments. This feedback is most important for improving the business, gaining more profit, and understanding the customer’s interest. Here we are using hybrid machine learning algorithms for efficient accuracy. Finally, from our research, logistic regression accuracy is 92%, XGBoost accuracy is 90%, decision trees predict 90% accuracy and random forest predicts 95.5% accuracy. Compared to the ensemble learning model, the random forest tree model performs a higher accuracy rate than ensemble models.

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Ensemble Machine Learning Models-Based Predictions for Sentimental Analysis on Twitter Data

  • 会议论文

  • 发表日期：

    2025年

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  • Sreenivas Mekala,
  • Y. Rohita,
  • K. Arun Sai,
  • G. Khaushik,
  • Krishna Vamshi Prabhu,
  • Subhani Shaik

摘要

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. In this process, input is so simple but deriving this information is too difficult. Internet data usage is increasing worldwide, using this data for feedback purposes. Such type of data classification and organization was most difficult for sentiments. This feedback is most important for improving the business, gaining more profit, and understanding the customer’s interest. Here we are using hybrid machine learning algorithms for efficient accuracy. Finally, from our research, logistic regression accuracy is 92%, XGBoost accuracy is 90%, decision trees predict 90% accuracy and random forest predicts 95.5% accuracy. Compared to the ensemble learning model, the random forest tree model performs a higher accuracy rate than ensemble models.