Research on App User Intent Recognition Based on Fusion Model and Semantic Network
With the popularity of mobile Applications(Apps),a large number of unstructured Chinese user reviews have appeared in the application market.Identifying App user intent based on these reviews helps developers make targeted maintenance and improvement of App software.To accurately recognize user intent,this study proposes an App user intent recognition method based on fusion model and semantic network,named FSAUIR.First,FSAUIR uses the Baidu tool Senta to determine the emotional tendency of the reviews.It then introduces Robustly optimized Bidirectional Encoder Representation from Transformers approach(RoBERTa)-based fusion intent classification model,RoBERTa-BiGRU-Multiple Self-Attention+SoftPool(RBMS),which transforms user reviews into semantic feature representations through the RoBERTa model.These representations are input into a Bidirectional Gated Recurrent Unit(BiGRU)to extract the global contextual semantic information of the reviews.Simultaneously,the multiple self-attention and SoftPool mechanisms obtain more critical feature information,retaining the main features.Finally,the Softmax normalizes the features to obtain the intent classification results.Subsequently,FSAUIR employs the PositionRank model to extract keywords from reviews under each intent category,calculate the co-occurrence relationship between keywords,and construct a keywords semantic network to recognize user intent with finer granularity.Experimental results show that compared to BERT,RoBERTa,RoBERTa-CNN,and other models,the RBMS model exhibits superior classification performance on the manually labeled dataset.The model achieves accuracy,precision,recall,and F1 value of 87.75％,88.09％,87.80％,and 87.88％,respectively.Additionally,the semantic network constructed by FSAUIR efficiently mines valuable information from user reviews in the intent classification result set.
万方数据
维普
