Deep Learning Approach for Aspect-Based Sentiment Classification: A Comparative Review

Trisna, Komang Wahyu and Jie, Huang Jin (2022) Deep Learning Approach for Aspect-Based Sentiment Classification: A Comparative Review. Applied Artificial Intelligence, 36 (1). ISSN 0883-9514

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Abstract

The emergence of various e-commerce sites has led to an increase in review sites for various services and products. People nowadays easily get information about products and services that will be used through reviews. Here sentiment analysis plays an important role in classifying the polarity of product reviews. However, with a large number of reviews, a sentiment analysis that only gives overall polarity is not sufficient. This will make it difficult to find the reviews of certain aspects (features) of the product. Aspect-based sentiment analysis as fine-grained sentiment analysis is able to provide specific polarity for each aspect contained in a sentence. Various kinds of development methods have been carried out to provide accurate results in aspect-based sentiment analysis. This paper will discuss the various deep learning methods that have been carried out and provide the possibility of research that can be carried out from Aspect-Based Sentiment Analysis.

Item Type: Article
Subjects: Digital Academic Press > Computer Science
Depositing User: Unnamed user with email support@digiacademicpress.org
Date Deposited: 14 Jun 2023 07:52
Last Modified: 18 May 2024 07:56
URI: http://science.researchersasian.com/id/eprint/1468

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