针对传统文本情感分析获取词向量信息不充分以及情感资源未得到充分利用,导致在判断评论情感极性所依赖的文本信息不足,提出ERNIE-BiGRU-Attention民航旅客评价情感分类模型.首先,应用简单数据增强技术(easy data augmentation,EDA)对数据 集进行处理.然后 基于预训练语 言模型(enhanced representation through knowledge integration,ERNIE)对文本进行情感知识提取.在特征提取方面,引入双向门控循环单元(bi-directional gate recurrent unit,BiGRU)与注意力机制.结果表明,该模型在分类上表现优异,综合F1为0.975 9,准确率较对比模型提升0.73％.
Sentiment Analysis of Civil Aviation Passenger Reviews Based on ERNIE-BiGRU and Attention Mechanism
In response to the insufficient information obtained from traditional text sentiment analysis,as well as the underutilization of emotional resources and other textual information,which leads to inadequate text information for determining the sentiment polarity of comments,an ERNIE-BiGRU-Attention model was proposed for sentiment classification of civil aviation passenger evaluations.Firstly,Easy Data Augmentation(EDA)was applied to enhance the dataset.Subsequently,based on Baidu's proposed pre-trained language model,Enhanced Representation through Knowledge Integration(ERNIE),emotional knowledge was extracted from the text.In terms of passenger evaluation feature extraction,Bi-directional Gate Recurrent Unit(BiGRU)was utilized and an attention mechanism was incorporated to extract features from the input.Finally,The results indicate that the proposed model performs excellently in classification,achieving a binary classification accuracy of 97.21％,a comprehensive F1 average of 0.975 9,and a 0.73％improvement in accuracy compared to the baseline model.
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