From prediction to explanation: managing influential negative reviews through explainable AI

TL;DR

This paper introduces a novel explainable AI algorithm designed to identify and explain influential negative online reviews, helping businesses better understand and respond to customer feedback.

Contribution

The study presents a new XAI algorithm that effectively identifies influential negative reviews and provides transparent explanations at feature and word levels.

Findings

Algorithm validated on 101,338 reviews

Provides understandable explanations for reviews

Enables strategic response to negative feedback

Abstract

The profound impact of online reviews on consumer decision-making has made it crucial for businesses to manage negative reviews. Recent advancements in artificial intelligence (AI) technology have offered businesses novel and effective ways to manage and analyze substantial consumer feedback. In response to the growing demand for explainablility and transparency in AI applications, this study proposes a novel explainable AI (XAI) algorithm aimed at identifying influential negative reviews. The experiments conducted on 101,338 restaurant reviews validate the algorithm's effectiveness and provides understandable explanations from both the feature-level and word-level perspectives. By leveraging this algorithm, businesses can gain actionable insights for predicting, perceiving, and strategically responding to online negative feedback, fostering improved customer service and mitigating the potential damage caused by negative reviews.