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A Novel Aspect-Based Deep Learning Framework (ADLF) to Improve Customer Experience

Tewari, Saurav, Pathak, Pramod and Stynes, Paul (2021) A Novel Aspect-Based Deep Learning Framework (ADLF) to Improve Customer Experience. In: Big Data Analytics: 9th International Conference, BDA 2021, Virtual Event, December 15-18, 2021, Proceedings. Lecture Notes in Computer Science (LNCS 13147) . Springer Nature Switzerland AG, pp. 119-130. ISBN 978-3-030-93620-4

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Official URL: https://doi.org/10.1007/978-3-030-93620-4_10

Abstract

Restaurateurs manage the customer experience of a restaurant through the overall rating of reviews on platforms such as Yelp, Google, and TripAdvisor. The challenge is to identify aspects of the restaurant to improve based on a deeper analysis of restaurant reviews. This research proposes a Novel Aspect-Based Deep Learning Framework (ADLF) to improve the customer experience of restaurants based on the value of Key Performance Indicators (KPIs) derived from the sentiment of restaurant reviews. The proposed framework combines an information retrieval algorithm, Okapi BM25 and a deep learning model, word2vec-cnn. The model is trained on the Yelp dataset that consists of 600,000 reviews. Key Performance Indicator’s (KPIs) are identified to help a restaurateur improve customer experience based on the sentiment of restaurant reviews. Five predetermined aspects namely flavor, cost, ambience, hygiene, and service are used to create the KPIs. Results demonstrate that diners express positive sentiment about “service” and negative sentiment about “cost”. The proposed framework achieved an accuracy of 94% and AUROC of 0.98. This novel framework, ADLF, shows promise for providing restaurateurs with a way to mine the unstructured textual opinion of their customers into KPIs that allows them to improve the customer experience of a restaurant.

Item Type: Book Section
Uncontrolled Keywords: Sentiment analysis; Deep learning Customer experience; Restaurant reviews; Yelp reviews; Information retrieval; Key performance indicators; Aspect-based sentiment analysis
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science

H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Hospitality Industry
Divisions: School of Computing > Staff Research and Publications
Related URLs:
Depositing User: Clara Chan
Date Deposited: 04 Jan 2022 15:57
Last Modified: 15 Mar 2022 12:04
URI: https://norma.ncirl.ie/id/eprint/5258

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