A Oversampling Method for Online Reviews Based on Fusion Text Sentiment Transfer
In recent years,the online review section has been destroyed by the abuse of"praise cashback"and"review review",which has seriously affected the performance of online review sentiment analysis model in real application scenarios.Therefore,we propose an online review oversampling method based on fusion text sentiment transfer.This method combines feature dictionary-based and deep learning-based methods to achieve text sentiment transfer.Feature dictionary-based method was used to identify and replace explicit sen-timent expression in most class samples.At the same time,the deep learning-based approach replaces the implicit sentiment expression that the former cannot hit by adding mask self-attention mechanism to the Seq2Seq model.Then,the restrictive EDA method is used to further expand the text as an enhanced text for a few class samples.The experimental results on the real data set show that the accuracy and Fl value of the proposed method are improved by 16.6％and 9.5％respectively,and the model's resolution to minority samples is improved by 12.2％.Compared with the traditional method,it also has better performance improvement for the trained model.
text sentiment transferunbalanced online reviewsfeature dictionarymasked self-attentionSeq2Seq
万方数据
维普
