A Memory-Driven Neural Attention Model for Aspect-Based Sentiment Classification

Authors

  • Jonathan van de Ruitenbeek Erasmus School of Economics, Erasmus University Rotterdam, 3062 PA Rotterdam, the Netherlands
  • Flavius Frasincar Erasmus School of Economics, Erasmus University Rotterdam, 3062 PA Rotterdam, the Netherlands
  • Gianni Brauwers Erasmus School of Economics, Erasmus University Rotterdam, 3062 PA Rotterdam, the Netherlands

DOI:

https://doi.org/10.13052/jwe1540-9589.2163

Keywords:

aspect sentiment classification, sentiment analysis, deep learning, attention models

Abstract

Sentiment analysis techniques are becoming more and more important as the number of reviews on the World Wide Web keeps increasing. Aspect-based sentiment analysis (ABSA) entails the automatic analysis of sentiments at the highly fine-grained aspect level. One of the challenges of ABSA is to identify the correct sentiment expressed towards every aspect in a sentence. In this paper, a neural attention model is discussed and three extensions are proposed to this model. First, the strengths and weaknesses of the highly successful CABASC model are discussed, and three shortcomings are identified: the aspect-representation is poor, the current attention mechanism can be extended for dealing with polysemy in natural language, and the design of the aspect-specific sentence representation is upheld by a weak construction. We propose the Extended CABASC (E-CABASC) model, which aims to solve all three of these problems. The model incorporates a context-aware aspect representation, a multi-dimensional attention mechanism, and an aspect-specific sentence representation. The main contribution of this work is that it is shown that attention models can be improved upon using some relatively simple extensions, such as fusion gates and multi-dimensional attention, which can be implemented in many state-of-the-art models. Additionally, an analysis of the parameters and attention weights is provided.

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Author Biographies

Jonathan van de Ruitenbeek, Erasmus School of Economics, Erasmus University Rotterdam, 3062 PA Rotterdam, the Netherlands

Jonathan van de Ruitenbeek received the B.S. degree in econometrics and operations research in 2017 and the M.S. degree in econometrics and management science in 2018 from Erasmus University Rotterdam, Rotterdam, the Netherlands. He presently works as a Data Specialist at ABN AMRO Verzekeringen, where he is responsible for the data delivery to the risk management.

Flavius Frasincar, Erasmus School of Economics, Erasmus University Rotterdam, 3062 PA Rotterdam, the Netherlands

Flavius Frasincar received the M.S. degree in computer science, in 1996, and the M.Phil. degree in computer science, in 1997, from Politehnica University of Bucharest, Bucharest, Romania, and the P.D.Eng. degree in computer science, in 2000, and the Ph.D. degree in computer science, in 2005, from Eindhoven University of Technology, Eindhoven, the Netherlands.

Gianni Brauwers, Erasmus School of Economics, Erasmus University Rotterdam, 3062 PA Rotterdam, the Netherlands

Gianni Brauwers received the B.S. degree in econometrics and operations research in 2019 and the M.S. degree in econometrics and management science in 2021 from Erasmus University Rotterdam, Rotterdam, the Netherlands. From 2019 till 2020, he was a Research Assistant at Erasmus University Rotterdam, focusing his research on neural attention models and sentiment analysis. He currently works as a researcher at ABF Research.

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Published

2022-11-09

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Articles