Ensemble Creation via Anchored Regularization for Unsupervised Aspect Extraction

TL;DR

This paper introduces an ensemble regularization method to improve unsupervised aspect extraction in sentiment analysis, demonstrating that regularized ensembles outperform rule-based ensembles and individual models.

Contribution

It proposes a novel anchored regularization technique to enhance unsupervised aspect extraction, improving upon existing models like ABAE.

Findings

Regularized ensemble outperforms rule-based ensemble.

Ensemble methods outperform individual models.

Regularization improves aspect extraction accuracy.

Abstract

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.