Adversarial Training for a Hybrid Approach to Aspect-Based Sentiment Analysis

Ron Hochstenbach, Flavius Frasincar, Maria Mihaela Truşcǎ*

*Corresponding author for this work

Research output: Chapter/Conference proceedingConference proceedingAcademicpeer-review

Abstract

The increasing popularity of the Web has subsequently increased the abundance of reviews on products and services. Mining these reviews for expressed sentiment is beneficial for both companies and consumers, as quality can be improved based on this information. In this paper, we consider the state-of-the-art HAABSA++ algorithm for aspect-based sentiment analysis tasked with identifying the sentiment expressed towards a given aspect in review sentences. Specifically, we train the neural network part of this algorithm using an adversarial network, a novel machine learning training method where a generator network tries to fool the classifier network by generating highly realistic new samples, as such increasing robustness. This method, as of yet never in its classical form applied to aspect-based sentiment analysis, is found to be able to considerably improve the out-of-sample accuracy of HAABSA++: for the SemEval 2015 dataset, accuracy was increased from 81.7% to 82.5%, and for the SemEval 2016 task, accuracy increased from 84.4% to 87.3%.

Original languageEnglish
Title of host publication22nd International Conference on Web Information Systems Engineering (WISE 2021)
EditorsWenjie Zhang, Lei Zou, Zakaria Maamar, Lu Chen
PublisherSpringer-Verlag
Pages291-305
Number of pages15
ISBN (Print)9783030915599
DOIs
Publication statusPublished - 2021
Event22nd International Conference on Web Information Systems Engineering, WISE 2021 - Melbourne, Australia
Duration: 26 Oct 202129 Oct 2021

Publication series

SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13081 LNCS
ISSN0302-9743

Conference

Conference22nd International Conference on Web Information Systems Engineering, WISE 2021
Country/TerritoryAustralia
CityMelbourne
Period26/10/2129/10/21

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

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