TY - GEN
T1 - A Novel Aspect-Based Deep Learning Framework (ADLF) to Improve Customer Experience
AU - Tewari, Saurav
AU - Pathak, Pramod
AU - Stynes, Paul
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Aspect-based sentiment analysis
KW - Customer experience
KW - Deep learning
KW - Information retrieval
KW - Key performance indicators
KW - Restaurant reviews
KW - Sentiment analysis
KW - Yelp reviews
UR - https://www.scopus.com/pages/publications/85122563845
U2 - 10.1007/978-3-030-93620-4_10
DO - 10.1007/978-3-030-93620-4_10
M3 - Conference contribution
AN - SCOPUS:85122563845
SN - 9783030936198
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 119
EP - 130
BT - Big Data Analytics - 9th International Conference, BDA 2021, Proceedings
A2 - Srirama, Satish Narayana
A2 - Lin, Jerry Chun-Wei
A2 - Bhatnagar, Raj
A2 - Agarwal, Sonali
A2 - Reddy, P. Krishna
PB - Springer Science and Business Media Deutschland GmbH
T2 - 9th International Conference on Big Data Analytics, BDA 2021
Y2 - 15 December 2021 through 18 December 2021
ER -