Existing aspect based sentiment analysis (ABSA) approaches leverage various neural network models to extract the aspect sentiments via learning aspect-specific feature representations. However, these approaches heavily rely on manual tagging of user reviews according to the predefined aspects as the input, a laborious and time-consuming process. Moreover, the underlying methods do not explain how and why the opposing aspect level polarities in a user review lead to the overall polarity. In this paper, we tackle these two problems by designing and implementing a new Multiple-Attention Network (MAN) approach for more powerful ABSA without the need for aspect tags using two new tag-free data sets crawled directly from TripAdvisor ({https://www.tripadvisor.com}). With the Self- and Position-Aware attention mechanism, MAN is capable of extracting both aspect level and overall sentiments from the text reviews using the aspect level and overall customer ratings, and it can also detect the vital aspect(s) leading to the overall sentiment polarity among different aspects via a new aspect ranking scheme. We carry out extensive experiments to demonstrate the strong performance of MAN compared to other state-of-the-art ABSA approaches and the explainability of our approach by visualizing and interpreting attention weights in case studies.

中文翻译：

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迈向无标签方面的情感分析：一种多注意力网络方法

现有的基于方面的情感分析 (ABSA) 方法利用各种神经网络模型通过学习特定于方面的特征表示来提取方面情感。然而，这些方法严重依赖于根据作为输入的预定义方面手动标记用户评论，这是一个费力且耗时的过程。此外，底层方法没有解释用户评论中相反的方面级别极性如何以及为什么会导致整体极性。在本文中，我们通过为更强大的 ABSA 设计和实施一种新的多注意力网络 (MAN) 方法来解决这两个问题，而无需使用两个新的无标签数据集直接从 TripAdvisor ({https:/ /www.tripadvisor.com}）。通过自我和位置感知注意机制，MAN 能够使用方面级别和整体客户评分从文本评论中提取方面级别和整体情绪，还可以通过新的方面排名方案检测导致不同方面整体情绪极性的重要方面. 我们进行了广泛的实验，以通过可视化和解释案例研究中的注意力权重来证明 MAN 与其他最先进的 ABSA 方法相比的强大性能以及我们方法的可解释性。