Short text Sentiment Analysis Model Based on Parallel Hybrid Network
Aiming at the problems that traditional word embeddings do not adequately express emotional seman-tics,feature mining is not comprehensive,and accuracy is low in the process of semantic sentiment analysis of short texts,a MACGRU parallel hybrid network model based on multi-head attention mechanism is proposed.First,accord-ing to the different characteristics of Capsule Network(CapsNet)and Bidirectional Gated Recurrent Unit(BiGRU),BERT word embedding and Glove word embedding are selected to vectorize short texts,and position embedding and part-of-speech embedding are added to the improvement of Glove word embedding,so that the short text obtains ric-her short text information in the word embedding stage.Secondly,the word vector trained by BERT and the word vec-tor trained by Glove are input into CapsNet and BiGRU to extract the local semantic information of the short text and the contextual semantic information of the short text.Then,after the feature output of CapsNet and BiGRU,a multi-head attention mechanism is added to weigh the extracted emotional features.Finally,the local features and contextual semantic features weighted by the multi-head attention mechanism are fused and output by the softmax function for e-motional classification.The model was experimentally verified on the public data set COVID-19,and its accuracy,precision,recall,and F1 indicators have all reached more than 95%.Compared with other benchmark models,the per-formance of the model is better,which fully proves its superiority.
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