Comparison of Ensemble Learning Methods for Mining the Implementation of the 7 Ps Marketing Mix on TripAdvisor Restaurant Customer Review Data

Abstract

The 7P marketing mix encompasses various business facets, notably the Process element governing internal operations from production to customer service. With the surge in online customer feedback, assessing machine learning efficacy, especially ensemble learning, in classifying 7P-related customer review data has gained prominence. This research aims to fill a gap in existing literature by evaluating ensemble learning’s performance on 7P classification, an area not extensively explored despite prior sentiment analysis studies. Employing a methodology merging Natural Language Processing (NLP) with ensemble learning, the study processes restaurant reviews using NLP techniques and employs ensemble learning for precision and accuracy. Findings demonstrate that DESMI yielded the highest performance metrics with accuracy at 0.697, precision at 0.699, recall at 0.697, and an F1-score of 0.684. These outcomes underscore ensemble learning's potential in handling complex datasets, signifying its relevance for marketers and researchers seeking comprehensive insights from customer reviews within the 7P marketing mix domain. This study sheds light on how ensemble learning outperforms its foundational methods, indicating its prowess in extracting meaningful insights from diverse and intricate customer feedback.