基于文本摘要提取的双路情感分析模型

Parallel Emotion Analysis Model Based on Text Abstract Extraction

针对传统文本分类模型存在识别能力受限、训练时间随着输入长度倍增的问题,提出了一种基于文本摘要提取的双路特征情感分析模型(BLAT)。BLAT模型引入Fastformer的加性注意力机制代替Transfomer的自注意力机制,使得模型能够在不损失精度的情况下,面对长文本训练能够有较为出色的训练速度。模型通过对原始文本数据做摘要提取处理形成双路特征,融入长短期记忆网络与卷积神经网络组成双路特征提取网络,实现对文本情感倾向的高效识别。通过实验在中文电商评论数据集上进行验证,准确率可以达到92.26%,相较当下主流模型能够达到较好的效果。

Aiming at the problems that the traditional text classification model has limited recognition ability and the training time doubles with the input length.A parallel feature extraction emotion analysis model BLAT based on text abstract extraction is proposed.BLAT model introduces additive attention mechanism of Fastformer to replace self attention mechanism of Transformer,so that the model can have an excellent training speed in the face of long text training without losing accuracy.Secondly,the model forms parallel features by extracting the original text data,and integrates the long-term and short-term memory network and convolutional neural network to form a parallel feature extraction network to realize the efficient recognition of text emotional tendency.It verifies on the Chinese e-commerce review data set,the accuracy can reach 92.26%,which can achieve better results compared with the current mainstream model.