JAIST Repository
https://dspace.jaist.ac.jp/Title

クラスタリングに基づく弱教師あり学習によるレビューの暗黙的属性の分類

Author(s)AYE AYE MARCitation
Issue Date

2024-03

TypeThesis or Dissertation

Text version

ETDURLhttp://hdl.handle.net/10119/19067

RightsDescription

Supervisor: 白井 清昭, 先端科学技術研究科, 博士

Japan Advanced Institute of Science and Technology

Implicit Aspect Classification of Online Reviews by Clustering-based WeakSupervision
1920405 Aye Aye Mar
Aspect Term Extraction (ATE) which is a process of extracting an aspect (alsoknown as opinion target) from a customer review sentence plays a vital rolein Aspect-Based Sentiment Analysis (ABSA). Many previous work of ATE
focused on explicit aspect but only a few work considered to extract implicit
aspects. However, customer reviews containing implicit aspects arewidespread on the Web (such as Amazon.com) and these sentences are also
important to fully understand the opinions and sentiments of the customers.
One of the bottleneck problem of implicit aspect extraction is lack ofa large dataset of reviews annotated with implicit aspects. Although thecorpus annotated with implicit aspects is required for every domain due todifferent types of aspects in different domains, constructing a corpus is labour
intensive and time consuming. Therefore, a system to automatically construct
a dataset annotated with implicit aspects is required. This study proposed anovel approach that automatically constructs a dataset annotated with implicit
aspects using unlabelled Amazon reviews to address the challenge of implicit
aspect extraction. To the best of our knowledge, no prior work has been
performed on the automatic construction of such a dataset.
The goal of this study is to develop a system of ATE for implicit
aspects. A dataset labeled with implicit aspects is automatically constructedby guessing implicit aspects in unlabeled review sentences. The proposed
method involves clustering review sentences labeled with explicit aspects(which were extracted by CRF model trained on golden explicit reviewsentences) and unlabeled review sentences. In this study, using a K-meansclustering approach with a relatively large number of clusters (10% of total
review sentences) aims to generate many small but accurate clusters.
Cluster labels, considered as implicit aspects, are automaticallyassigned based on the assumption that sentences with similar context share acommon aspect. When selecting the most relevant cluster label among theexplicit aspects in the cluster, the frequency of the aspect in the list of aspects
extracted by CRF and its occurrence in the review sentences within the cluster
are considered to determine the relevance of the chosen cluster label. When
there is more than one aspect that can be the cluster label, we did not consider
such kind of cluster since the cluster label is not unique. Moreover, thereliability of the cluster label to be chosen was determined by the thresholdvalue (Tr). Unlabeled sentences in clusters matching pre-defined implicit
aspect categories are then obtained as implicit-aspect-labeled sentences. To1

increase the number of clusters related with the implicit aspects, the aspectsynonym list was identified.
The accuracy of the constructed corpus was evaluated by a humanannotator by checking manually on 50 random sentences for each implicit
aspect. The results showed that accuracy of the sentences in the constructed
corpus was reasonably high, i.e., from 0.58 to 0.82. The study presents
findings and observations regarding with constructing the corpus annotatedwith implicit aspects.
In this study, implicit aspect extraction problem is formulated asclassification problem. Then, BERT model is fine-tuned for implicit aspect
classification using the constructed dataset by investigating the best valuesof hyper-parameters. Experiments results of implicit aspect classification
show that our method achieves 82% and 84% accuracy for the mobile phone
and PC reviews respectively, which are 20 and 21 percentage points higher
than the baseline.
Furthermore, the study explores the impact of explicit review
sentences for implicit aspect classification by combining the explicit
sentences and implicit sentences and then by training classification model on
the combined dataset. The experimental results showed that it further boosts
the performance of implicit aspect classification in both phone and PC
domain.
Keywords: Aspect-based Sentiment Analysis, Aspect Extraction,
Implicit Aspect, Weakly-supervised Learning, Online Review2