Cluster computing technologies are rapidly advancing and user-generated online reviews are booming in the current Internet and e-commerce environment. The latest question–answering (Q&A)-style reviews are novel, abundant and easily digestible product reviews that also contain massive valuable information for customers. In this paper, we mine valuable aspect information of products contained in these reviews on GPU clusters. To achieve this goal, we utilize two subtasks of aspect-based sentiment analysis: aspect term extraction (ATE) and aspect category classification (ACC). Most previous works focused on only one task or solved these two tasks separately, even though they are highly interrelated, and they do not make full use of abundant training resources. To address this problem, we propose a novel multi-task neural learning model to jointly handle these two tasks and explore the performance of our model on GPU clusters. We conducted extensive comparative experiments on an annotated corpus and found that our proposed model outperforms several baseline models in ATE and ACC tasks on GPU clusters, yielding significant strides in data mining for these types of reviews.

在当前的Internet和电子商务环境中，群集计算技术正在迅速发展，并且用户生成的在线评论正在蓬勃发展。最新的问答式评论是新颖，丰富且易于消化的产品评论，其中还包含大量有价值的客户信息。在本文中，我们在GPU群集上挖掘了这些评论中包含的产品的有价值的方面信息。为了实现此目标，我们利用基于方面的情感分析的两个子任务：方面术语提取（ATE）和方面类别分类（ACC）。以前的大多数工作都只关注一个任务，或者分别解决了这两个任务，即使它们之间具有很高的相关性，也没有充分利用丰富的培训资源。为了解决这个问题，我们提出了一种新颖的多任务神经学习模型来共同处理这两个任务，并探索我们的模型在GPU集群上的性能。我们在带注释的语料库上进行了广泛的比较实验，发现我们提出的模型在GPU集群上的ATE和ACC任务中优于几种基线模型，在这些类型的评论的数据挖掘方面取得了长足进步。