ABSTRACT
Aiming at the shortcomings of high cost, low efficiency and coarse granularity of traditional consumer demand sequencing methods in practical applications, this paper proposes an online consumer demand ranking and content prediction method based on Kano model. First, this method combines SnowNLP sentiment analysis and consumer attention to classify the demand attributes. Then, the probability of topic words under different demands is obtained by LDA topic model. Finally, the ARIMA model is introduced to predict the probability of topic words and sentiment values under different demands in the future, which can accurately and efficiently recognize the rapidly changing consumer demands. Example calculations show that this method can classify and sort consumer demands in a more fine-grained way, which provides a reference for consumer demand prediction research of enterprises and solves the problem of priority level of enterprise marketing resource allocation.
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Index Terms
- Research on Online Consumer Demand Ranking and Content Prediction Based on Kano Model
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