Aspect-Based Sentiment Analysis using Machine Learning and Deep Learning Approaches

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Sub tasks in aspect-based sentiment analysis
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Published: May 17, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i5s.6636
Keywords:
Aspect-based sentiment analysis, Machine learning, Sentiment analysis, Deep learning, Deep learning algorithms in SA

Main Article Content

Dinesh Kumar
Department of Computer Science & Engineering Krishna Institute of Engineering and Technology, Ghaziabad, Uttarpradesh, India
Avdhesh Gupta
Department of Information Technology Ajay Kumar Garg Engineering College, Ghaziabad, Uttarpradesh, India
Vishan Kumar Gupta
Department of Computer Science & Engineering Sir Padampat Singhania University, Udaipur, Rajasthan, India
Ashotosh Gupta
Department of Computer Science and Engineering Sir Padampat Singhania University, Udaipur, Rajasthan, India

Abstract

Sentiment analysis (SA) is also known as opinion mining, it is the process of gathering and analyzing people's opinions about a particular service, good, or company on websites like Twitter, Facebook, Instagram, LinkedIn, and blogs, among other places. This article covers a thorough analysis of SA and its levels. This manuscript's main focus is on aspect-based SA, which helps manufacturing organizations make better decisions by examining consumers' viewpoints and opinions of their products. The many approaches and methods used in aspect-based sentiment analysis are covered in this review study (ABSA). The features associated with the aspects were manually drawn out in traditional methods, which made it a time-consuming and error-prone operation. Nevertheless, these restrictions may be overcome as artificial intelligence develops. Therefore, to increase the effectiveness of ABSA, researchers are increasingly using AI-based machine learning (ML) and deep learning (DL) techniques. Additionally, certain recently released ABSA approaches based on ML and DL are examined, contrasted, and based on this research, gaps in both methodologies are discovered. At the conclusion of this study, the difficulties that current ABSA models encounter are also emphasized, along with suggestions that can be made to improve the efficacy and precision of ABSA systems.

Article Details

How to Cite
Kumar, D. ., Gupta, A. ., Gupta, V. K. ., & Gupta, A. . (2023). Aspect-Based Sentiment Analysis using Machine Learning and Deep Learning Approaches. International Journal on Recent and Innovation Trends in Computing and Communication, 11(5s), 118–138. https://doi.org/10.17762/ijritcc.v11i5s.6636
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Vol. 11 No. 5s (2023): Special Issue on Advances in Computing Systems for Web-Based Machine Learning Applications
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Articles

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