To better select online teaching platforms,give college students a better online course learning experience,and provide a reference for future online education and teaching,a method for selecting online teaching platforms based on online reviews is proposed.Firstly,user reviews from alternative online teaching platforms are collected by the crawler technology,and NL PIR-ICTCLAS Chinese word separation system is used to separate online words.Next,attribute word extraction is conducted using TF-IDF algorithm,along with a method that was manually selected to obtain the attribute set.The weights of attributes are determined using the mean square deviation method.Subsequently,sentiment analysis is carried out on the online reviews,with user emotional orientations represented as probability distributions regarding the evaluation scale.On this basis,the extended VIKOR method is used to select the optimal online teaching platform.Finally,the feasibility of the method proposed in this paper is demonstrated through an example and comparative analysis.
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