Aspect-Based Sentiment Analysis for Hospitality Industry Applications: A Systematic Literature Review

Abstract

One of the most important methods in Natural Language Processing (NLP), especially in the hospitality sector, is Aspect-Based Sentiment Analysis (ABSA). The fine-grained analysis of user-generated information that the method allows enables organizations to comprehend attitudes related to particular characteristics, such as amenities, cleanliness, and service quality. This study presents a holistic literature analysis of ABSA methodology, datasets, and applications in the hospitality industry with 57 research papers published within the last seven years. Some significant developments are the use of pre-trained transformer models, multimodal analysis that combines textual and visual data, and creative aspect extraction methods. However, the problems still exist, such as multilingual data deficiency, implicit aspect detection, and huge language model computation cost. The paper gives an extensive review of ABSA in enhancing customer happiness through actionable insights derived from sentiment analysis, underlining existing trends and gaps.