论文标题
朝着基于无标记方面的情感分析：一种多重注意网络方法
Toward Tag-free Aspect Based Sentiment Analysis: A Multiple Attention Network Approach
论文作者
论文摘要
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方面的特征表示来提取方面情感。但是，这些方法在很大程度上依赖于根据预定义的方面作为输入，费力且耗时的过程的用户评论的手动标记。此外，基础方法并不能解释用户审查中相对方面的极性如何以及为什么会导致整体极性。在本文中，我们通过设计和实施一种新的多重意见网络（MAN）方法来解决这两个问题，以实现更强大的ABSA，而无需使用两个直接从TripAdvisor捕获的两个新的Free-Free数据集（{https://wwww.tripadvisor.com}）。借助自我意识和位置的注意力机制，Man能够使用方面级别和整体客户评级从文本评论中提取方面水平和整体情感，并且还可以通过新方面的排名方案来检测到重要方面的重要方面。与其他最先进的ABSA方法相比，我们进行了广泛的实验，以证明人的出色表现以及通过在案例研究中可视化和解释注意力的重量的解释性。
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.
