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Aspect-based sentiment analysis (ABSA), which focuses on extracting and assessing thoughts expressed toward specific characteristics or entities inside of a given text, has emerged as a result of Natural Language Processing (NLP). It has become crucial to comprehend how individuals feel about multiple elements of goods, services, or activities for businesses and scholars alike because to the ever-growing number of user-generated content on the internet.This review paper provides an extensive overview of cutting-edge techniques and methodologies employed in ABSA, with a specific focus on restaurant reviews. We explore the key challenges faced in this field, including aspect extraction, sentiment classification, and domain adaptation, with a specific emphasis on the unique aspects relevant to restaurant reviews.These evaluations cover a wide range of topics, including cost, ambience, meal quality, and service. The review provides a thorough examination of cutting-edge methods with a focus on implicit aspect identification in restaurant reviews.As a result, this review aims to underscore the significance of detecting implicit aspects in ABSA, particularly in the context of restaurant reviews. By presenting the advancements achieved in ABSA for restaurant sentiment analysis and identifying research directions specific to this domain, we analyze different techniques of ABSA in this paper.
Singhi et al. (Fri,) studied this question.