Aspect Based Sentiment Analysis is the most granular form of sentiment
analysis that can be performed on the documents / sentences. Besides delivering
the most insights at a finer grain, it also poses equally daunting challenges.
One of them being the shortage of labelled data. To bring in value right out of
the box for the text data being generated at a very fast pace in today's world,
unsupervised aspect-based sentiment analysis allows us to generate insights
without investing time or money in generating labels. From topic modelling
approaches to recent deep learning-based aspect extraction models, this domain
has seen a lot of development. One of the models that we improve upon is ABAE
that reconstructs the sentences as a linear combination of aspect terms present
in it, In this research we explore how we can use information from another
unsupervised model to regularize ABAE, leading to better performance. We
contrast it with baseline rule based ensemble and show that the ensemble
methods work better than the individual models and the regularization based
ensemble performs better than the rule-based one.

本文探讨了如何使用来自其他无监督模型的信息来规范 ABAE，以获得更好的性能，既在规则基础的合集方法上得到对比，也证明了对比于单个模型，集成模型的效果更好，而基于规则的集成模型则表现不如基于规范化的集成模型。