Ensemble learning is a powerful technique for combining multiple classifiers to achieve improved performance. However, the challenge of applying ensemble learning to dynamic and diverse data, such as text in sentiment analysis, has limited its effectiveness. In this paper, we propose a novel reinforcement learning-based method for integrating base learners in sentiment analysis. Our method modifies the influence of base learners on the ensemble output based on the problem space, without requiring prior knowledge of the input domain. This approach effectively manages the dynamic behavior of data to achieve greater efficiency and accuracy. Unlike similar methods, our approach eliminates the need for basic knowledge about the input domain. Our experimental results demonstrate the robust performance of the proposed method compared to traditional methods of base learner integration. The significant improvement in various evaluation criteria highlights the effectiveness of our method in handling diverse data behavior. Overall, our work contributes a novel reinforcement learning-based approach to improve the effectiveness of ensemble learning in sentiment analysis.

集成学习是一种强大的技术，可以组合多个分类器以提高性能。然而，将集成学习应用于动态和多样化数据（例如情感分析中的文本）的挑战限制了其有效性。在本文中，我们提出了一种基于强化学习的新颖方法，用于将基础学习器集成到情感分析中。我们的方法根据问题空间修改基学习器对集成输出的影响，而不需要输入域的先验知识。这种方法有效地管理数据的动态行为，以实现更高的效率和准确性。与类似的方法不同，我们的方法不需要输入域的基本知识。我们的实验结果证明了与传统的基础学习器集成方法相比，所提出的方法具有稳健的性能。各种评估标准的显着改进凸显了我们的方法在处理不同数据行为方面的有效性。总的来说，我们的工作提供了一种新颖的基于强化学习的方法，以提高情感分析中集成学习的有效性。