User Innovation Review Extraction Method Based on Two-layer Attention Mechanism
E-commerce platforms have accumulated a large number of product reviews published by users,which contain rich product innovation ideas that can provide creativity and inspiration for product designers.However,traditional review mining techniques have low accuracy when identifying innovative reviews with complex semantics and irregularities,which cannot meet the actual needs.To this end,we propose a two-layer BiLSTM model based on a two-layer attention mechanism to extract innovation reviews with high accuracy.The model first uses a screening EDA data augmentation technology to expand innovative reviews,and then combines the word vector attention mechanism on the basis of Bert pre-training model to generate word semantics and context representation information,and then uses two-layer BiLSTM to extract the timing features of review sentences.Finally,the attention mechanism is used to identify the key features of review sentences,so as to accurately identify innovation reviews.Through the example verification of two product review data sets,it shows that the method proposed in this paper has higher recognition accuracy than the current innovation review extraction method,and the F1 value can reach 92％.
information extractiondeep learningtext classificatione-commerce platformonline reviews
NETL
NSTL
万方数据
