No abstract

Online product reviews are exploring on e-commerce platforms, and mining aspect-level product information contained in those reviews has great economic benefit. The aspect category classification task is a basic task for aspect-level sentiment analysis which has become a hot research topic in the natural language processing (NLP) field during the last decades. In various e-commerce platforms, there emerge various user-generated question-answering (QA) reviews which generally contain much aspect-related information of products. Although some researchers have devoted their efforts on the aspect category classification for traditional product reviews, the existing deep learning-based approaches cannot be well applied to represent the QA-style reviews. Thus, we propose a 4-dimension (4D) textual representation model based on QA interaction-level and hyperinteraction-level by modeling with different levels of the text representation, i.e., word-level, sentence-level, QA interaction-level, and hyperinteraction-level. In our experiments, the empirical studies on datasets from three domains demonstrate that our proposals perform better than traditional sentence-level representation approaches, especially in the Digit domain.

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.