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Artificial Intelligence Review

Erschienen in:

01.03.2021

Mining and classifying customer reviews: a survey

verfasst von: L. D. C. S. Subhashini, Yuefeng Li, Jinglan Zhang, Ajantha S. Atukorale, Yutong Wu

Erschienen in: Artificial Intelligence Review | Ausgabe 8/2021

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Abstract

With the increasing number of customer reviews on the Web, there is a growing need for effective methods to retrieve valuable information hidden in these reviews, as sellers need to gain a deep understanding of customers’ preferences in a timely manner. With the continuous enhancement of opinion mining or sentiment analysis research, researchers have proposed many automatic mining and classification methods. However, how to choose a trusted method is a difficult problem for companies, because customer reviews (or opinions) contain a lot of uncertain information and noise. This article reports on a detailed survey of recent opinion mining literature. It also reviews how to extract text features in opinions that may contain noise or uncertainties, how to express knowledge in opinions, and how to classify them. Through this extensive study, this paper discusses open questions and recommends future research directions for building the next generation of opinion mining systems.

Vorheriger Artikel Scientometric analysis of literature on distributed vehicular networks : VOSViewer visualization techniques

Nächster Artikel A systematic review on overfitting control in shallow and deep neural networks

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Titel: Mining and classifying customer reviews: a survey
verfasst von: L. D. C. S. Subhashini
Yuefeng Li
Jinglan Zhang
Ajantha S. Atukorale
Yutong Wu
Publikationsdatum: 01.03.2021
Verlag: Springer Netherlands
Erschienen in: Artificial Intelligence Review / Ausgabe 8/2021
Print ISSN: 0269-2821
Elektronische ISSN: 1573-7462
DOI: https://doi.org/10.1007/s10462-021-09955-5

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