Aspect-based Opinion Summary (AOS), consisting of aspect discovery and sentiment classification steps, has recently been emerging as one of the most crucial data mining tasks in e-commerce systems. Along this direction, the LDA-based model is considered as a notably suitable approach, since this model offers both topic modeling and sentiment classification. However, unlike traditional topic modeling, in the context of aspect discovery it is often required some initial seed words, whose prior knowledge is not easy to be incorporated into LDA models. Moreover, LDA approaches rely on sampling methods, which need to load the whole corpus into memory, making them hardly scalable. In this research, we study an alternative approach for AOS problem, based on Autoencoding Variational Inference (AVI). Firstly, we introduce the Autoencoding Variational Inference for Aspect Discovery (AVIAD) model, which extends the previous work of Autoencoding Variational Inference for Topic Models (AVITM) to embed prior knowledge of seed words. This work includes enhancement of the previous AVI architecture and also modification of the loss function. Ultimately, we present the Autoencoding Variational Inference for Joint Sentiment/Topic (AVIJST) model. In this model, we substantially extend the AVI model to support the JST model, which performs topic modeling for corresponding sentiment. The experimental results show that our proposed models enjoy higher topic coherent, faster convergence time and better accuracy on sentiment classification, as compared to their LDA-based counterparts.
翻译:以外观为基础的《意见摘要》(AOS)由方方面面发现和情绪分类步骤组成,最近逐渐成为电子商务系统中最重要的数据挖掘任务之一。沿着这一方向,基于LDA的模型被视为一种特别合适的方法,因为这一模型既提供主题建模,又提供情绪分类。然而,与传统的专题建模不同,在外观发现方面,通常需要一些初始的种子词,其先前的知识不容易纳入LDA模型。此外,LDA方法依赖取样方法,需要将整个体加到记忆中,使其难以伸缩。在这一研究中,我们研究了一种基于自动编码变异变异(AVI)的AOS问题的替代精确方法。首先,我们引入了Aspect Discover(AVIAAD)的自动编码变异变异(AVIAVI)模型,该模型先前的变异模型推介(AVITM)将先前的模型推延到先前的种子文字知识中。 这项工作包括加强AVI结构结构,并修改AVI的一致的AVST结果。