Personalized tag recommender systems recommend a series of tags for items by leveraging users' historical records, which helps tag-aware recommender systems (TRS) to better depict user profiles and item characteristics. However, existing personalized tag recommendation solutions are insufficient to capture the collaborative signal hidden in the interactions among entities without considering reasonable correlations, since neighborhood messages are treated as the same weights when constructing graph-structured data, resulting in decreased accuracy in making recommendations. In this paper, we propose a Tag-aware Attentional Graph Neural Network (TA-GNN), which integrates the attention mechanism into tag-based graph neural networks to alleviate the above issues. Specifically, we extract the user-tag interaction and the item-tag interaction from the user-tag-item graph structure. For each interaction, we exploit the contextual semantics of multi-hop neighbors by leveraging attentional strategy on graph neural networks to discriminate the importance of different connected nodes. In this way, we effectively extract collaborative signals of neighborhood representations and capture the potential information in an explicit manner. Extensive experiments on three public datasets show that our proposed TA-GNN outperforms the state-of-the-art personalized tag recommendation baselines.