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.

中文翻译：

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科学编程中的大数据管理和分析：一种基于深度学习的问答式评论方面类别分类方法

在线产品评论正在电子商务平台上进行探索，挖掘评论中包含的方面级产品信息具有巨大的经济效益。方面类别分类任务是方面级别情感分析的基础任务，在过去的几十年中已成为自然语言处理（NLP）领域的热门研究课题。在各种电子商务平台中，出现了各种用户生成的问答（QA）评论，这些评论通常包含很多与产品方面相关的信息。尽管一些研究人员致力于传统产品评论的方面类别分类，但现有的基于深度学习的方法不能很好地应用于表示 QA 风格的评论。因此，我们提出了一种基于 QA 交互级别和超交互级别的 4 维（4D）文本表示模型，通过对不同级别的文本表示进行建模，即单词级别、句子级别、QA 交互级别和超交互-等级。在我们的实验中，对来自三个领域的数据集的实证研究表明，我们的提议比传统的句子级表示方法表现更好，尤其是在数字领域。