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.

本文提出了基于 Autoencoding Variational Inference 的模型，用于 Aspect-based Opinion Summary (AOS) 中的 aspect discovery 和 sentiment classification 问题，并成功应用于 AVIAD 模型和 AVIJST 模型中，实验结果表明相比 LDA 模型，我们的模型具有更高的主题一致性、更快的收敛时间和更好的情感分类精度。