[1]张华辉,邱晓莹,徐航.文本情感分类方法研究综述[J].延边大学学报(自然科学版),2023,(03):275-282.
 ZHANG Huahui,QIU Xiaoying,XU Hang.Research overview on text sentiment classification methods[J].Journal of Yanbian University,2023,(03):275-282.
点击复制

文本情感分类方法研究综述

参考文献/References:

[1] 周春梅,冯林,张华辉.网络舆情对新冠疫情下青少年情感态度的分析[J].计算机仿真,2023,40(1):553 - 558.
[2] 董理,王中卿,熊德意.基于文本信息的股票指数预测[J].北京大学学报(自然科学版),2017,53(2):273 - 278.
[3] 钟佳娃,刘巍,王思丽,等.文本情感分析方法及应用综述[J].数据分析与知识发现,2021,5(6):1 - 13.
[4] 祝清麟,梁斌,徐睿峰,等.结合金融领域情感词典和注意力机制的细粒度情感分析[J].中文信息学报,2022,36(8):109 - 117.
[5] 闫晓东,黄涛.基于情感词典的藏语文本句子情感分类[J].中文信息学报,2018,32(2):75 - 80.
[6] 杨书新,张楠.融合情感词典与上下文语言模型的文本情感分析[J].计算机应用,2021,41(10):2829 - 2834.
[7] 刘志明,刘鲁.基于机器学习的中文微博情感分类实证研究[J].计算机工程与应用,2012,48(1):1 - 4.
[8] 汪海燕,黎建辉,杨风雷.支持向量机理论及算法研究综述[J].计算机应用研究,2014,31(5):1281 - 1286.
[9] 王立志,慕晓冬,刘宏岚.采用改进粒子群优化的SVM方法实现中文文本情感分类[J].计算机科学,2020,47(1):231 - 236.
[10] 张越兵,苗夺谦,张志飞.基于三支决策的多粒度文本情感分类模型[J].计算机科学,2017,44(12):188 - 193.
[11] 徐继伟,杨云.集成学习方法:研究综述[J].云南大学学报(自然科学版),2018,40(6):1082 - 1092.
[12] 康雁,李浩,梁文韬,等.针对文本情感分类任务的textSE - ResNeXt集成模型[J].计算机工程与应用,2020,56(7):205 - 209.
[13] 贺鸣,孙建军,成颖.基于朴素贝叶斯的文本分类研究综述[J].情报科学,2016,34(7):147 - 154.
[14] 杨鼎,阳爱民.一种基于情感词典和朴素贝叶斯的中文文本情感分类方法[J].计算机应用研究,2010,27(10):3737 - 3739.
[15] 尹春勇,章荪.面向短文本情感分类的端到端对抗变分贝叶斯方法[J].计算机应用,2020,40(9):2536 - 2542.
[16] 洪巍,李敏.文本情感分析方法研究综述[J].计算机工程与科学,2019,41(4):750 - 757.
[17] 杨玉亭.基于BERT的方面级短文本情感分类方法研究[D].成都:四川师范大学,2021.
[18] 李彦冬,郝宗波,雷航.卷积神经网络研究综述[J].计算机应用,2016,36(9):2508 - 2515.
[19] 孟佳娜,吕品,于玉海,等.基于CNN的方面级跨领域情感分析研究[J].计算机工程与应用,2022,58(16):175 - 183.
[20] 郑诚,曹源,薛满意.面向方面级情感分类的多层注意网络[J].计算机工程与应用,2020,56(19):176 - 181.
[21] 郑诚,魏素华,曹源.结合语法信息的BG - CNN用于方面级情感分类[J].计算机工程与应用,2022,58(5):148 - 155.
[22] 刘建伟,宋志妍.循环神经网络研究综述[J].控制与决策,2022,37(11):2753 - 2768.
[23] 曾锋,曾碧卿,韩旭丽,等.基于双层注意力循环神经网络的方面级情感分析[J].中文信息学报,2019,33(6):108 - 115.
[24] 邓钰,李晓瑜,崔建,等.用于短文本情感分类的多头注意力记忆网络[J].计算机应用,2021,41(11):3132 - 3138.
[25] DEVLIN J, CHANG M W, LEE K, et al.BERT: pre - training of deep bidirectional transformers for language understanding[C]//Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies(Volume 1: Long and Short Papers).Minneapolis: Association for Computational Linguistics, 2019:4171 - 4186.
[26] 王昆,郑毅,方书雅,等.基于文本筛选和改进BERT的长文本方面级情感分析[J].计算机应用,2020,40(10):2838 - 2844.
[27] 罗俊,陈黎飞.基于BERT的不完全数据情感分类[J].计算机应用,2021,41(1):139 - 144.
[28] 胡任远,刘建华,卜冠南,等.融合BERT的多层次语义协同模型情感分析研究[J].计算机工程与应用,2021,57(13):176 - 184.
[29] 马帅,刘建伟,左信.图神经网络综述[J].计算机研究与发展,2022,59(1):47 - 80.
[30] 王光,李鸿宇,邱云飞,等.基于图卷积记忆网络的方面级情感分类[J].中文信息学报,2021,35(8):98 - 106.
[31] 王启发,周敏,王中卿,等.基于用户与产品信息和图卷积网络的情感分类研究[J].中文信息学报,2021,35(3):134 - 142.
[32] 李浩,张兰,杨兵,等.融合双重权重机制和图卷积神经网络的微博细粒度情感分类[J].计算机科学,2022,49(3):246 - 254.
[33] TANG H, JI D H, LI C L, et al.Dependency graph enhanced dual - transformer structure for aspect - based sentiment classification[C]//Proceedings of the 58th annual meeting of the association for computational linguistics.Online: Association for Computational Linguistics, 2020:6578 - 6588.
[34] 杨玉亭,冯林,代磊超,等.面向上下文注意力联合学习网络的方面级情感分类模型[J].模式识别与人工智能,2020,33(8):753 - 765.
[35] 何丽,房婉琳,张红艳.基于上下文保持能力的方面级情感分类模型[J].模式识别与人工智能,2021,34(2):157 - 166.
[36] TANG D Y, QIN B, FENG X C, et al.Effective LSTMs for target - dependent sentiment classification[C]//Proceedings of COLING 2016, the 26th international conference on computational linguistics: technical papers.Osaka: The COLING 2016 Organizing Committee, 2016:3298 - 3307.
[37] WANG Y Q, HUANG M L, ZHAO L, et al.Attention - based LSTM for aspect - level sentiment classification[C]//Proceedings of the 2016 conference on empirical methods in natural language processing.Austin: Association for Computational Linguistics, 2016:606 - 615.
[38] MA D H, LI S J, ZHANG X D, et al.Interactive attention networks for aspect - level sentiment classification[C]//Proceedings of the twenty - sixth international joint conference on artificial intelligence.Melbourne: International Joint Conferences on Artificial Intelligence Organization, 2017:4068 - 4074.
[39] CHEN P, SUN Z Q, BING L D, et al.Recurrent attention network on memory for aspect sentiment analysis[C]//Proceedings of the 2017 conference on empirical methods in natural language processing.Copenhagen: Association for Computational Linguistics, 2017:452 - 461.
[40] LI X, BING L D, LAM W, et al.Transformation networks for target - oriented sentiment classification[C]//Proceedings of the 56th annual meeting of the association for computational linguistics(Volume 1: Long Papers).Melbourne: Association for Computational Linguistics, 2018:946 - 956.
[41] HUANG B X, OU Y L, CARLEY K M.Aspect level sentiment classification with attention - over - attention neural networks[C]//Social,cultural, and behavioral modeling.Cham: Springer International Publishing, 2018:197 - 206.
[42] FAN F F, FENG Y S, ZHAO D Y.Multi - grained attention network for aspect - level sentiment classification[C]//Proceedings of the 2018 conference on empirical methods in natural language processing.Brussels: Association for Computational Linguistics, 2018:3433 - 3442.
[43] ZHANG C, LI Q C, SONG D W.Aspect - based sentiment classification with aspect - specific graph convolutional networks[C]//Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing(EMNLP - IJCNLP).Hong Kong: Association for Computational Linguistics, 2019:4568 - 4578.
[44] SONG Y W, WANG J H, JIANG T, et al.Targeted sentiment classification with attentional encoder network[C]//Artificial neural networks and machine learning - ICANN 2019: text and time series.Cham: Springer International Publishing, 2019:93 - 103.
[45] XU H, LIU B, SHU L, et al.BERT post - training for review reading comprehension and aspect - based sentiment analysis[C]//Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies(Volume 1: Long and Short Papers).Minneapolis: Association for Computational Linguistics, 2019:2324 - 2335.
[46] ZENG B Q, YANG H, XU R Y, et al.LCF: A local context focus mechanism for aspect - based sentiment classification[J].Applied Sciences, 2019,9(16):3389.
[47] 杨善良,常征.基于图注意力神经网络的中文隐式情感分析[J].计算机工程与应用,2021,57(24):161 - 167.
[48] 袁景凌,丁远远,潘东行,等.基于时序和上下文特征的中文隐式情感分类模型[J].计算机应用,2021,41(10):2820 - 2828.
[49] 张军,张丽,沈凡凡,等.RoBERTa融合BiLSTM及注意力机制的隐式情感分析[J].计算机工程与应用,2022,58(23):142 - 150.
[50] 陈秋嫦,赵晖,左恩光,等.上下文感知的树递归神经网络下隐式情感分析[J].计算机工程与应用,2022,58(7):167 - 175.
[51] 杜永萍,贺萌,赵晓铮.基于Wasserstein距离分层注意力模型的跨域情感分类[J].模式识别与人工智能,2019,32(5):446 - 454.
[52] YU J F, GONG C J, XIA R.Cross - domain review generation for aspect - based sentiment analysis[C]//Findings of the association for computational linguistics: ACL - IJCNLP 2021.Online: Association for Computational Linguistics, 2021:4767 - 4777.
[53] DU C N, SUN H F, WANG J Y, et al.Adversarial and domain - aware BERT for cross - domain sentiment analysis[C]//Proceedings of the 58th annual meeting of the association for computational linguistics.Online: Association for Computational Linguistics, 2020:4019 - 4028.

备注/Memo

收稿日期: 2023-03-29
基金项目: 国家自然科学基金(62103209); 福建省自然科学基金(2020J05213); 福建省中青年教师教育科研项目(JAT220298)
作者简介: 张华辉(1995—),男,硕士,助教,研究方向为自然语言处理.

更新日期/Last Update: 2023-09-20