结合多种语言学特征的中文隐式情感分类  

Chinese Implicit Sentiment Classification Combining Multiple Linguistic Features

作　　者：陆靓倩 王中卿[1] 周国栋[1] LU Liangqian;WANG Zhongqing;ZHOU Guodong(School of Computer Science and Technology,Soochow University,Suzhou,Jiangsu 215006,China)

机构地区：[1]苏州大学计算机科学与技术学院,江苏苏州215006

出　　处：《计算机科学》2023年第12期255-261,共7页Computer Science

基　　金：国家自然科学基金(62076175,61976146)。

摘　　要：情感分析一直是自然语言处理中的热点研究方向,隐式情感分类指无显式情感词的情感分类任务,目前,隐式情感分析还处于起步阶段。隐式情感分析面临缺乏显式情感词、表达方式委婉、语义难以理解等问题,传统的情感分析方法如情感词典、词袋模型等难以生效,使得隐式情感分类任务更加艰巨。针对以上问题,提出了一种结合文本、词性与依存关系的图神经网络模型来进行隐式情感分类。具体来说,模型首先抽取文本的词性和依存特征,然后使用预训练语言模型BERT提取文本向量特征,从而构建了一个基于多种语言学特征的图注意力神经网络。该模型在SMP2021隐式情感识别公开数据集上进行了多次实验。实验结果表明,相较于多种基线模型,所提模型取得了较好的分类效果,证实了所提出的融合了多种语言学特征的隐式情感分类方法具有可行性和有效性。Sentiment analysis has always been a hot research direction in natural language processing.Implicit sentiment classification refers to the task of sentiment classification without explicit sentiment words.At present,implicit sentiment analysis is still in its infancy.Implicit sentiment analysis is faced with problems such as lack of explicit sentiment words,euphemism of expression,and difficulty in understanding semantics.Traditional sentiment analysis methods,such as sentiment dictionary and bag-of-word models,are difficult to be effective,making the task of implicit sentiment classification more difficult.To solve the above problems,this paper proposes a graph neural network model that combines text,part-of-speech tags and dependency to perform implicit sentiment classification.Specifically,the model first extracts part of speech and dependency features of the text,and then uses pre-training language model BERT to extract text vector features,thus builds a graph attention neural network based on multiple linguistic features.The model has been tested on SMP2021 implicit sentiment recognition public dataset for several times.Experimental results show that the proposed model achieves the best results compared with multiple baseline models.The proposed implicit sentiment classification method is feasible and effective.

关 键 词：隐式情感分类 词性标注 依存分析 图模型 BERT 语言学特征 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]