Interactive Attention Networks for Aspect-Level Sentiment Classification This paper proposes an Interactive Attention Network (IAN) for aspect-level sentiment classification. Aspect-level sentiment classification aims to identify the sentiment polarity of specific targets in their context. Previous approaches have focused on generating target-specific representations to model contexts, but often ignored the separate modeling of targets. The IAN model argues that both targets and contexts should be modeled separately but interactively to enhance sentiment classification performance. The IAN model uses LSTM networks to obtain hidden states of words for targets and contexts. It then computes attention vectors to capture important information from the context and target. The model uses these attention vectors to generate representations for targets and contexts, which are concatenated for final sentiment classification. The IAN model is trained using cross-entropy with L2 regularization. Experiments on the SemEval 2014 dataset show that IAN achieves state-of-the-art performance in aspect-level sentiment classification. The model outperforms other baselines such as LSTM, TD-LSTM, AE-LSTM, and ATAE-LSTM. The IAN model's interactive attention mechanism allows it to better capture the relationship between targets and contexts, leading to improved sentiment classification accuracy. The IAN model is evaluated on the SemEval 2014 dataset, which includes reviews in the Restaurant and Laptop categories. The model's performance is compared with other baselines, and it achieves the best results. The model's effectiveness is further validated through case studies, where it successfully identifies important words for sentiment classification. The IAN model is a significant advancement in aspect-level sentiment classification, as it effectively models both targets and contexts interactively. The model's attention mechanism allows it to focus on important words and phrases, leading to improved sentiment classification accuracy. The IAN model's performance on the SemEval 2014 dataset demonstrates its effectiveness in aspect-level sentiment classification.Interactive Attention Networks for Aspect-Level Sentiment Classification This paper proposes an Interactive Attention Network (IAN) for aspect-level sentiment classification. Aspect-level sentiment classification aims to identify the sentiment polarity of specific targets in their context. Previous approaches have focused on generating target-specific representations to model contexts, but often ignored the separate modeling of targets. The IAN model argues that both targets and contexts should be modeled separately but interactively to enhance sentiment classification performance. The IAN model uses LSTM networks to obtain hidden states of words for targets and contexts. It then computes attention vectors to capture important information from the context and target. The model uses these attention vectors to generate representations for targets and contexts, which are concatenated for final sentiment classification. The IAN model is trained using cross-entropy with L2 regularization. Experiments on the SemEval 2014 dataset show that IAN achieves state-of-the-art performance in aspect-level sentiment classification. The model outperforms other baselines such as LSTM, TD-LSTM, AE-LSTM, and ATAE-LSTM. The IAN model's interactive attention mechanism allows it to better capture the relationship between targets and contexts, leading to improved sentiment classification accuracy. The IAN model is evaluated on the SemEval 2014 dataset, which includes reviews in the Restaurant and Laptop categories. The model's performance is compared with other baselines, and it achieves the best results. The model's effectiveness is further validated through case studies, where it successfully identifies important words for sentiment classification. The IAN model is a significant advancement in aspect-level sentiment classification, as it effectively models both targets and contexts interactively. The model's attention mechanism allows it to focus on important words and phrases, leading to improved sentiment classification accuracy. The IAN model's performance on the SemEval 2014 dataset demonstrates its effectiveness in aspect-level sentiment classification.