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Research on online shopping behavior based on Long Short-Term Memory and Latent Dirichlet Allocation

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DOI: 10.23977/AICT2020045


Haochen He, Liang Xie, Lu Liu, Zhihan Xu

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In this paper, we use natural language processing and machine learning methods to extract product and user characteristics from product descriptions and reviews to establish the sigmoid function dynamic weight factor evaluation model to evaluate the product's success in the market. When establishing the evaluation model, we fully consider the star rating, helpfulness vote, view submitted by the customer. When carrying out quantitative processing of the view, Nltk, Word2vec and Long Short-Term Memory (LSTM) are used to extract the emotional polarity of the text. We compare the results of the model with the actual situation to verify the accuracy of the model. In addition to the above work, we also use the Latent Dirichlet Allocation (LDA) model to investigate whether some words in the view submitted by customers are clearly associated with star rating.


Word2vec; Long Short-Term Memory; Sigmoid function dynamic weight factor evaluation model; Latent Dirichlet Allocation

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