基于词级和语义级注意力的细粒度情感分析模型

Fine-Grained Sentiment Analysis Model with Both Word-Level and Semantic-Level Attention

针对细粒度情感分析任务中仅使用语义级特征导致分类效果差的问题,提出一种基于词级和语义级注意力的细粒度情感分析模型.将模型应用在SemEval-2014 Task4数据集上进行实验,与AEN-BERT和BERT-SPC模型相比,该模型正确率分别提升了3.51%和2.17%,与采用Glove词向量的模型相比,本文模型的正确率提高了3%~4%.在细粒度情感分析任务中引入词级特征可以进一步丰富文本的特征表示,通过注意力机制可以将上下文中与情感分析相关词汇的权重加强,提高细粒度情感分析的准确性.

Aiming at the problem of poor classification effect caused by only using semantic-level features in fine-grained emotion analysis task,a fine-grained emotion analysis model based on word-level and semantic-level attention was proposed.When applied to SemEVAL-2014 Task4 dataset,the accuracy of this model is improved by 3.51%and 2.17%compared with AEN-BERT and BERT-SPC models,respectively.Compared with the model using Glove word vector,the accuracy of this model is improved by 3%-4%.The introduction of word-level features in fine-grained sentiment analysis tasks can further enrich text feature representation,and the weight of words related to sentiment analysis can be strengthened through the attention mechanism to improve the accuracy of fine-grained sentiment analysis.