From prediction to explanation: managing influential negative reviews through explainable AI
Fuente:
arXiv
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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866912686061649920 |
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| author | Shen, Rongping |
| author_facet | Shen, Rongping |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_19692 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | From prediction to explanation: managing influential negative reviews through explainable AI Shen, Rongping Computers and Society 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. |
| title | From prediction to explanation: managing influential negative reviews through explainable AI |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2412.19692 |