Jianheng Li and Lirong Chen, Inner Mongolia University, China

[Research background] With the continuous development of society, consumers pay more attention to the key information of product fine-grained attributes when shopping. [Research purposes] This study will fine tune the Sentence-BERT word embedding model and LDA model, mine the subject characteristics in online reviews of goods, and show consumers the details of various aspects of goods. [Research methods] First, the Sentence-BERT model was fine tuned in the field of e-commerce online reviews, and the online review text was converted into a word vector set with richer semantic information; Secondly, the vectorized word set is input into the LDA model for topic feature extraction; Finally, focus on the key functions of the product through keyword analysis under the theme. [Results] This study compared this model with other word embedding models and LDA models, and compared it with common topic extraction methods. The theme consistency of this model is 0.5 higher than that of other models, which improves the accuracy of theme extraction.

E-commerce comments,LDA model,Sentence-BERT,Topic extraction,Text clustering