Open Access

ARTICLE

A Semi-Supervised Approach for Aspect Category Detection and Aspect Term Extraction from Opinionated Text

by Bishrul Haq¹, Sher Muhammad Daudpota¹, Ali Shariq Imran², Zenun Kastrati^3,*, Waheed Noor⁴

1 Department of Computer Science, Sukkur IBA University, Sukkur, 65200, Pakistan
2 Department of Computer Science, Norwegian University of Science and Technology (NTNU), Gjøvik, 2815, Norway
3 Department of Informatics, Linnaeus University, Växjö, 35195, Sweden
4 Department of Computer Science & IT, University of Balochistan, Quetta, 87300, Pakistan

* Corresponding Author: Zenun Kastrati. Email:

(This article belongs to the Special Issue: Advance Machine Learning for Sentiment Analysis over Various Domains and Applications)

Computers, Materials & Continua 2023, 77(1), 115-137. https://doi.org/10.32604/cmc.2023.040638

Received 26 March 2023; Accepted 13 June 2023; Issue published 31 October 2023

Abstract

The Internet has become one of the significant sources for sharing information and expressing users’ opinions about products and their interests with the associated aspects. It is essential to learn about product reviews; however, to react to such reviews, extracting aspects of the entity to which these reviews belong is equally important. Aspect-based Sentiment Analysis (ABSA) refers to aspects extracted from an opinionated text. The literature proposes different approaches for ABSA; however, most research is focused on supervised approaches, which require labeled datasets with manual sentiment polarity labeling and aspect tagging. This study proposes a semi-supervised approach with minimal human supervision to extract aspect terms by detecting the aspect categories. Hence, the study deals with two main sub-tasks in ABSA, named Aspect Category Detection (ACD) and Aspect Term Extraction (ATE). In the first sub-task, aspects categories are extracted using topic modeling and filtered by an oracle further, and it is fed to zero-shot learning as the prompts and the augmented text. The predicted categories are the input to find similar phrases curated with extracting meaningful phrases (e.g., Nouns, Proper Nouns, NER (Named Entity Recognition) entities) to detect the aspect terms. The study sets a baseline accuracy for two main sub-tasks in ABSA on the Multi-Aspect Multi-Sentiment (MAMS) dataset along with SemEval-2014 Task 4 sub-task 1 to show that the proposed approach helps detect aspect terms via aspect categories.

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Keywords

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