Natural Language Inference Model for Customer Advocacy Detection in Online Customer Engagement

Abstract

Online customer advocacy has developed as a distinctive strategic way to improve organisational performance by fostering favourable reciprocal affinitive customer behaviours between the business and its customers. Intelligent systems that can identify online social advocates based on their social interaction and long-standing conversations with the brads are still lacking. This study adds to the burgeoning body of literature in this research area by developing a novel model to identify brand advocates using natural language inference (NLI) and artificial intelligence (AI) approaches. In particular, a hybridised deep learning model (BERT-BiLSTM-TextCNN) is proposed and adept at extracting the amount of entailment, contradiction, and neutrality obtained from the advocates' replies to the brands. This offers a new dimension to identify advocates based on the semantic similarities between the brands’ tweets and customers’ replies. The experimental results demonstrate the applicability of integrating the advantages of fine-tuned BERT, TextCNN, and BiLSTM using various evaluation metrics. Further, the proposed model is incorporated in a downstream task to verify and validate its effectiveness in capturing the correlation between brands and their advocates. Our findings contribute to the burgeoning body of literature in this research area and have important implications for identifying and engaging with brand advocates in online customer engagement.