融合情感符号的自注意力BLSTM情感分析  

Self-attention BLSTM Emotion Analysis with Emotion Symbols

作　　者：刘臣[1] 方结 郝宇辰 LIU Chen;FANG Jie;HAO Yu-chen(School of Management,University of Shanghai For Science and Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学管理学院,上海200093

出　　处：《软件导刊》2020年第3期39-43,共5页Software Guide

基　　金：国家自然科学基金项目(7177411,71303157)。

摘　　要：利用深度学习方法进行情感分析时,将文本作为一个整体进行编码,缺乏对表情符号与情感词的有效关注。而传统基于词典的方法则过分依赖于情感词典与判断规则的质量,不能充分考虑文本深层语义关系。针对该问题,构建融合表情符号与情感词的自注意力模型。通过BLSTM训练得到情感符号,并与文本特征向量融合,同时引入结构化自注意力机制识别文本中不同情感符号的情感信息。在NLPCC2014和微博公开语料数据集上的实验表明,相较传统情感分析方法,该模型可有效提高情感分类准确率。In current emotion analysis tasks,deep learning usually encodes the text as a whole and lacks effective attention to emoticons and emotion words.The traditional dictionary-based approach relies too much on the quality of affective dictionaries and judgment rules and fails to fully consider the deep semantic relationship of texts.In this paper,a self-attention model integrating emoticons and emoticons is constructed,and emoticons and text features are obtained through BLSTM learning for fusion.At the same time,the self-attention mechanism is introduced to recognize the emotional information of different emotive symbols in the text.Experiments on NLPCCW 2014 and the Weibo public corpus data set show that compared with previous methods of emotion analysis,the model in this paper can effectively improve the accuracy of emotion classification.

关 键 词：情感分析 情感符号 注意力机制 双向长短期记忆网络 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]