The paper "Interactive Attention Networks for Aspect-Level Sentiment Classification" addresses the challenge of identifying the sentiment polarity of specific targets within their context. Traditional approaches often overlook the separate modeling of targets, focusing instead on generating target-specific representations through various methods. The authors propose an Interactive Attention Network (IAN) that learns attentions in both the context and target, separately generating representations for each. This approach leverages the interaction between targets and contexts to enhance sentiment classification performance. The IAN model uses LSTM networks and attention mechanisms to capture important information from both the context and the target. Experimental results on the SemEval-2014 dataset demonstrate the effectiveness of the IAN model, showing superior performance compared to other baseline methods. The paper also includes a case study to illustrate how IAN assigns attention weights to words in the context and target, providing insights into the model's decision-making process.The paper "Interactive Attention Networks for Aspect-Level Sentiment Classification" addresses the challenge of identifying the sentiment polarity of specific targets within their context. Traditional approaches often overlook the separate modeling of targets, focusing instead on generating target-specific representations through various methods. The authors propose an Interactive Attention Network (IAN) that learns attentions in both the context and target, separately generating representations for each. This approach leverages the interaction between targets and contexts to enhance sentiment classification performance. The IAN model uses LSTM networks and attention mechanisms to capture important information from both the context and the target. Experimental results on the SemEval-2014 dataset demonstrate the effectiveness of the IAN model, showing superior performance compared to other baseline methods. The paper also includes a case study to illustrate how IAN assigns attention weights to words in the context and target, providing insights into the model's decision-making process.