Double Triplet Network for Confusing Text Sentiment Classification
The rapid development of pre-trained models has made a breakthrough in the task of sentiment classifica-tion.However,there is a large number of semantically ambiguous and confusing text in the massive data provided by the Internet,which restricts the effect of most current classification models.To address this issue,a double triplet network for sentiment classification(DTN4SC)is proposed.This method improves the construction method of trip-let sample combinations,by extracting and weighing two kinds of triplet samples from straightforward text and or-dinary text,respectively,which captures the similarity between texts of the same category and the differences be-tween texts of confusing categories.And during the training process,the confusing text in one batch is added to the next batch for further training.Experimental results on nlpcc2014,waimai_10k and ChnSentiCorp show that the proposed method has better performance in accuracy and F1 value compared with the existing sentiment classifica-tion methods of confusing text,by 3.16%,2.35%and 2.5%improvements,respectively.
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