Title: An extended recommendation system by applying aspect-based sentiment analysis method on customers' review and experience

Authors: Thanh Ho; An-Dinh Van; Trinh Tran Thi Kieu; Anh Nguyen Thi Linh; Thao Huynh Nhi Thanh; Hieu Tran Nguyen Ngoc

Addresses: University of Economics and Law, Ho Chi Minh City, Vietnam; Vietnam National University, Ho Chi Minh City, Vietnam ' University of Economics and Law, Ho Chi Minh City, Vietnam; Vietnam National University, Ho Chi Minh City, Vietnam ' University of Economics and Law, Ho Chi Minh City, Vietnam; Vietnam National University, Ho Chi Minh City, Vietnam ' University of Economics and Law, Ho Chi Minh City, Vietnam; Vietnam National University, Ho Chi Minh City, Vietnam ' University of Economics and Law, Ho Chi Minh City, Vietnam; Vietnam National University, Ho Chi Minh City, Vietnam ' University of Economics and Law, Ho Chi Minh City, Vietnam; Vietnam National University, Ho Chi Minh City, Vietnam

Abstract: The hotel industry heavily relies on customer reviews and recommendations to attract guests and ensure customer satisfaction. However, the abundance of online reviews poses a challenge for users seeking relevant and reliable information for their hotel selection process. This study proposes a novel approach that leverages aspect-based sentiment analysis (ABSA) to assess the alignment between hotel sentiment scores and user preferences at the aspect level. By experimenting on 62,887 customer feedback in hospitality industry, two approaches are developed and presented: 1 calculating aspect-based sentiment hotel profiles using Wu-Palmer similarity, predefined index terms, and PyABSA; 2) extracting user preferences through term frequency (TF). The recommendation results are obtained by aligning hotel sentiment scores with user preferences using a value-focused approach (scalar product). Our evaluation, based on the Mean Reciprocal Rank (MRR) metric, reveals that, the most suitable hotel is within the top 5 recommended items, with an MRR value of 0.2849.

Keywords: aspect-based sentiment analysis; ABSA; recommendation system; context-awareness; preference extraction; customer experience; personalised recommendations.

DOI: 10.1504/IJIIDS.2024.141769

International Journal of Intelligent Information and Database Systems, 2024 Vol.16 No.4, pp.426 - 450

Received: 29 Aug 2023
Accepted: 07 Apr 2024
Published online: 01 Oct 2024 *

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