Deriving topic-related and interaction features to predict top attractive reviews for a specific business entity
Eunjung Lee and Huimin Zhao
| Year of publication: |
2020
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|---|---|
| Authors: | Lee, Eunjung ; Huimin, Zhao |
| Published in: |
Journal of business analytics. - London : Taylor & Francis Group, ISSN 2573-2358, ZDB-ID 2907637-7. - Vol. 3.2020, 1, p. 17-31
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| Subject: | latent topic model | machine learning | Online review helpfulness | predictive analytics | text mining | top attractive reviews | Künstliche Intelligenz | Artificial intelligence | Data Mining | Data mining | Prognoseverfahren | Forecasting model | Virales Marketing | Viral marketing | Online-Handel | Online retailing | Social Web | Social web |
| Type of publication: | Article |
|---|---|
| Type of publication (narrower categories): | Aufsatz in Zeitschrift ; Article in journal |
| Language: | English |
| Other identifiers: | 10.1080/2573234X.2020.1768808 [DOI] |
| Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10012255706