News Commentary Sentiment Analysis Method Based on BE-MCNN Model
Real time news comments have the characteristics of short text,rich information,and complex structure,making it difficult for sentiment analysis to accurately capture their true emotional tendencies.To enhance semantic feature information,reduce model overfitting problems,and improve the accuracy of news comment sentiment analysis,a news comment sentiment analysis algorithm is proposed that inte-grates BERT model,Transformer Encoder,and multi-scale CNN model.Firstly,in response to the short length of news comments and the high content of expressing emotional views,a BERT model is used to pre train news comment texts and obtain feature vectors with contextual information;Secondly,to solve the problem of model overfitting,a layer of Transformer encoder is added downstream of the BERT model;Fi-nally,a four channel dual layer CNN model is used to improve the performance of analyzing news comment sentiment by combining convolu-tional kernels of different sizes.The experimental results show that the accuracy of this method on two news comment datasets reaches 93.0％and 96.4％,respectively;The comparative experiments with different models further demonstrate the effectiveness of the proposed method.
万方数据
维普
