The paper introduces the Adaptive Recursive Neural Network (AdaRNN) for target-dependent Twitter sentiment classification. AdaRNN is designed to adaptively propagate the sentiments of words to specific targets based on context and syntactic relationships. It employs multiple composition functions and learns to select the most appropriate function for each word, improving the accuracy of sentiment classification. The authors also introduce a manually annotated dataset for target-dependent Twitter sentiment analysis, which is used to evaluate the effectiveness of AdaRNN. Experimental results show that AdaRNN outperforms baseline methods, demonstrating its ability to handle complex compositionalities in sentiment analysis. The paper includes a detailed explanation of the AdaRNN architecture, its training process, and the experimental setup, along with a comparison of different hyperparameters and their impact on performance.The paper introduces the Adaptive Recursive Neural Network (AdaRNN) for target-dependent Twitter sentiment classification. AdaRNN is designed to adaptively propagate the sentiments of words to specific targets based on context and syntactic relationships. It employs multiple composition functions and learns to select the most appropriate function for each word, improving the accuracy of sentiment classification. The authors also introduce a manually annotated dataset for target-dependent Twitter sentiment analysis, which is used to evaluate the effectiveness of AdaRNN. Experimental results show that AdaRNN outperforms baseline methods, demonstrating its ability to handle complex compositionalities in sentiment analysis. The paper includes a detailed explanation of the AdaRNN architecture, its training process, and the experimental setup, along with a comparison of different hyperparameters and their impact on performance.