The user preference identification for product improvement based on online comment patch
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Online comments have become a valuable source for designers for the purpose of product improvement. However, the implicitly expressed users’ preferences on multiple product attributes in incomplete online comments make it difficult to extract useful information to improve the product from the online comments. In order to identify the users’ preference from the perspective of product improvement, the comment extension mining model is proposed to patch up the online reviews based on the semantic similarity and emotional resemblance. First of all, for the sake of mining the full context semantic information of the keywords in comments, they are transferred into vectors by the word2vec method. Next, smart semantic distance measurement models are developed to match the online comments with the standard comment templates that express users’ sentiment on product attributes based on the semantic similarity. Moreover, the fine-grained matching neural networks are designed to further match the reviews of each product attribute to its standard sentiment templates according to the user’s overall emotions towards the product. Finally, the KANO model is introduced to depict users’ preferences and develop product improvement strategies. The experiment on laptop product confirms that our method is effective.
KeywordsOnline comments Comment extension mining model Fine-grained matching neural network Product improvement KANO model
This work was supported by the Chinese National Natural Science Foundation (No. 71871135).
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