文本情感分析方法研究综述
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摘要
文本情感是信息挖掘的一个新兴领域,近年受到管理学等相关领域的广泛关注。目前,文本情感分析使用的方法主要有情感词典方法和机器学习方法。由于文本情感分析的结果对优化政府、企业以及消费者决策具有重大意义,以文本情感分析的方法为视角,对情感词典的方法、有监督的机器学习方法和弱监督的深度学习方法以及其他方法的相关文献进行了梳理并做出评述。此外,指出虽然文本情感分析领域的学者基于情感词典和有监督的机器学习方法已提出许多情感分析模型,但准确率和效率普遍不高,进一步的研究重点应在于使用深度学习的方法处理文本情感,并提出未来的研究方向。
Text sentiment analysis is an emerging field of information mining,and it has attracted wide attention of management fields and other related areas in recent years.Currently,the methods used to perform text sentiment analysis tasks are mainly sentiment dictionary and t machine learning.The results of the text sentiment analysis are of great importance for the optimization of the decisions made by governments,enterprises and consumers.From the perspective of text sentiment analysis methods,we sort out and comment on methods such as sentiment dictionary,supervised machine learning,weak supervised deep learning,and some other methods.In addition,we find out that although a number of scholars in the field of text sentiment analysis have proposed many sentiment analysis models based on sentiment dictionary and supervised machine learning,the accuracy and efficiency of these models are generally not high.We suggest that further research should focus on using deep learning to deal with the text sentiment analysis,and also make proposal for future research directions.
Text sentiment analysis is an emerging field of information mining,and it has attracted wide attention of management fields and other related areas in recent years.Currently,the methods used to perform text sentiment analysis tasks are mainly sentiment dictionary and t machine learning.The results of the text sentiment analysis are of great importance for the optimization of the decisions made by governments,enterprises and consumers.From the perspective of text sentiment analysis methods,we sort out and comment on methods such as sentiment dictionary,supervised machine learning,weak supervised deep learning,and some other methods.In addition,we find out that although a number of scholars in the field of text sentiment analysis have proposed many sentiment analysis models based on sentiment dictionary and supervised machine learning,the accuracy and efficiency of these models are generally not high.We suggest that further research should focus on using deep learning to deal with the text sentiment analysis,and also make proposal for future research directions.
引文
[1]Whissell C.Objective analysis of text:II.Using an emotional compass to describe the emotional tone of situation comedies[J].Psychological Reports,1998,82(2):643-646.
[2]Whissell C.Using the revised dictionary of affect in language to quantity the emotional undertones of samples of natural language[J].Psychological Reports,2009,105(2):509-521.
[3]Li Shou-shan,Li Yi-wei,Huang Ju-ren,et al.Construction of Chinese sentiment lexicon using bilingual information and label propagation algorithm[J].Journal of Chinese Information Processing,2013,27(6):75-81.(in Chinese)
[4]Yang Ai-min,Lin Jiang-hao,Zhou Yong-mei.Method on building Chinese text sentiment lexicon[J].Journal of Frontiers of Computer Science and Technology,2013,7(11):1033-1039.(in Chinese)
[5]Rao Yang-hui,Lei Jing-sheng,Liu Wen-yin,et al.Building emotional dictionary for sentiment analysis of online news[J].World Wide Web-internet&Web Information Systems,2014,17(4):723-742.
[6]Wang Zhi-tao,Yu Zhi-wen,Guo Bin,et al.Sentiment analysis of Chinese micro blog based on lexicon and rule set[J].Computer Engineering and Applications,2015,51(8):218-225.(in Chinese)
[7]Zhou Jie.Research of sentiment analysis for the Chinese microblog based on emotional dictionary and sentence classification[D].Yinchuan:Ningxia University,2016.(in Chinese)
[8]Zhang Ke-liang,Huang Jin-zhu,Cao Rong,et al.Text sentimental orientation analysis based on HNC contextual framework and sentimental dictionaries[J].Journal of Shandong University(Natural Science),2016,51(7):51-58.(in Chinese)
[9]Zhou Zhe,Shang Lin.A sentiment analysis method based on dynamic lexicon and three-way decision[J].Journal of Shandong University(Engineering Science),2015,45(1):19-23.(in Chinese)
[10]Li Yu-qing,Li Xin,Han Xu,et al.A bilingual lexicon-based multi-class semantic orientation analysis for microblogs[J].Acta Electronica Sinica,2016,44(9):2068-2073.(in Chinese)
[11]Vilares D,Alonso M A,Gmez-Rodrguez C.Supervised sentiment analysis in multilingual environments[J].Information Processing and Management,2017,53(3):595-607.
[12]Sharma A,Dey S.A boosted SVM based ensemble classifier for sentiment analysis of online reviews[J].ACM SIGAPPApplied Computing Review,2013,13(4):43-52.
[13]Tang Xiao-bo,Yan Cheng-xi.A sentimental classification model based on SPIPRO principle and support vector machine[J].Information Studies:Theory&Application,2013,36(1):98-103.(in Chinese)
[14]Manek A S,Shenoy P D,Mohan M C,et al.Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier[J].World Wide Web-internet&Web Information Systems,2017,20(2):135-154.
[15]Abbasi A,France S,Zhu Zhang,et al.Selecting Attributes for sentiment classification using feature relation networks[J].IEEE Transactions on Knowledge&Data Engineering,2011,23(3):447-462.
[16]Han Kai-xu.Text sentiment analysis based on SVM[D].Daqing:Northeast Petroleum University,2014.(in Chinese)
[17]Shi Han-xiao,Zhan Wen-ping,Li Xiao-jun.A supervised fine-grained sentiment analysis system for online reviews[J].Intelligent Automation&Soft Computing,2015,21(4):589-605.
[18]Deng Zhi-hong,Luo Kun-hu,Yu Hong-liang.A study of supervised term weighting scheme for sentiment analysis[J].Expert Systems with Applications,2014,41(7):3506-3513.
[19]Perikos I,Hatzilygeroudis I.Recognizing emotions in text using ensemble of classifiers[J].Engineering Applications of Artificial Intelligence,2016,51:191-201.
[20]Tripathy A,Agrawal A,Rath S K.Classification of sentiment reviews using n-gram machine learning approach[J].Expert Systems with Applications,2016,57:117-126.
[21]Giatsoglou M,Vozalis M G,Diamantaras K I,et al.Sentiment analysis leveraging emotions and word embeddings[J].Expert Systems with Applications,2017,69:214-224.
[22]Tang Du-yu,Wei Fu-ru,Qin Bing,et al.Sentiment embeddings with applications to sentiment analysis[J].IEEETransactions on Knowledge&Data Engineering,2016,28(2):496-509.
[23]Fernández-Gavilanes M,álvarez-López T,Juncal-Martínez J,et al.Unsupervised method for sentiment analysis in online texts[J].Expert Systems with Applications,2016,58:57-75.
[24]Liang Bin,Liu Quan,Xu Jin,et al.Aspect-based sentiment analysis based on multi-attention CNN[J].Journal of Computer Research and Development,2017,54(8):1724-1735.(in Chinese)
[25]Liu Jin-shuo,Zhang Zhi.Sentiment analysis on food safety news using joint deep neural network model[J].Computer Science,2016,43(12):277-280.(in Chinese)
[26]Xie Tie,Zheng Xiao,Zhang Lei,et al.Sentiment classification of Chinese short text based on parallelized recursive neural network[J].Computer Applications and Software,2017,34(3):205-211.(in Chinese)
[27]Sun Xiao,Li Cheng-cheng,Ren Fu-ji.Sentiment analysis for Chinese microblog based on deep neural networks with convolutional extension features[J].Neurocomputing,2016,210(3):227-236.
[28]Cao Yu-hui.The research on text sentiment analysis based on deep learning[D].Harbin:Harbin Institute of Technology,2016.(in Chinese)
[29]Zhu Shao-jie.Research on text sentiment classification based on deep learning[D].Harbin:Harbin Institute of Technology,2014.(in Chinese)
[30]Zhou Ying,Liu Yue,Cai Jun.Sentiment analysis of microblogs based on attention mechanism[J].Information Studies:Theory&Application,2018,41(3):89-94.(in Chinese)
[31]Hu Fei,Li Li,Zhang Zi-li,et al.Emphasizing essential words for sentiment classification based on recurrent neural networks[J].Journal of Computer Science and Technology,2017,32(4):785-795.
[32]Ma Yu-kun,Peng Hai-yun,Khan T,et al.Sentic LSTM:Ahybrid network for targeted aspect-based sentiment analysis[J].Cognitive Computation,2018,10(4):639-650.
[33]Liang Jun,Chai Yu-mei,Yuan Hui-bin,et al.Polarity shifting and LSTM based recursive networks for sentiment analysis[J].Journal of Chinese Information Processing,2015,29(5):152-159.(in Chinese)
[34]Tian Zhu.Research on sentiment analysis based on deep feature representation[D].Weihai:Shandong University,2017.(in Chinese)
[35]Zhou Yu,Xu Rui-feng,Gui Lin.A sequence level latent topic modeling method for sentiment analysis via CNN based diversified restrict Boltzmann machine[C]∥Proc of International Conference on Machine Learning and Cybernetics,2016:356-361.
[36]Du Chang-shun,Huang Lei.Sentiment analysis with piecewise convolution neural network[J].Computer Engineering&Science,2017,39(1):173-179.(in Chinese)
[37]Xu Xiang-yu,Pan Jin-shan,Zhang Yu-jin,et al.Motion blur kernel estimation via deep learning[J].IEEE Transactions on Image Processing,2018,27(1):194-205.
[38]Zhang Zhi-hua.Deep learning based sentiment word vector towards sentiment analysis[D].Weihai:East China Normal University,2016.(in Chinese)
[39]Wu Dong-yin,Gui Lin,Chen Zhao,et al.Sentiment analysis based on deep representation learning and gaussian processes transfer learning[J].Journal of Chinese Information Processing,2017,31(1):169-176.(in Chinese)
[40]Gao Ge,Luo Jun-mei,Wang Yu.Analyzing textual sentiment based on HNC theory[J].Data Analysis and Knowledge Discovery,2017,1(8):85-91.(in Chinese)
[41]Pandey A C,Rajpoot D S,Saraswat M.Twitter sentiment analysis using hybrid cuckoo search method[J].Information Processing and Management,2017,53(4):764-779.
[42]Yang Li-gong,Tang Shi-ping,Zhu Jian.A markov logic network based sentence sentimental analysis method[J].Transactions of Beijing Institute of Technology,2013,33(6):600-604.(in Chinese)
[43]Zhao Yan-yan,Qin Bin,Che Wan-xiang,et al.Appraisal expression recognition based on syntactic path[J].Journal of Software,2011,22(5):887-898.(in Chinese)
[44]Fan Na,An Yi-sheng,Li Hui-xian.Research on analyzing sentiment of texts based on k-nearest neighbor algorithm[J].Computer Engineering and Design,2012,33(3):1160-1164.(in Chinese)
[45]Grandi U,Loreggia A,Rossi F,et al.A Borda count for collective sentiment analysis[J].Annals of Mathematics&Artificial Intelligence,2016,77(3-4):281-302.
[46]Eliacik A B,Erdogan N.Influential user weighted sentiment analysis on topic based microblogging community[J].Expert Systems with Applications,2018,92:403-418.
[47]Dragoni M,Petrucci G.A fuzzy-based strategy for multi-domain sentiment analysis[J].International Journal of Approximate Reasoning,2018,93(10):59-73.
[48]Yousefpour A,Ibrahim R,Hamed H N A.Ordinal-based and frequency-based integration of feature selection methods for sentiment analysis[J].Expert Systems with Applications,2017,75:80-93.
[49]Mukhtar N,Khan M A,Chiragh N.Effective use of evaluation measures for the validation of best classifier in Urdu sentiment analysis[J].Cognitive Computation,2017,9(4):446-456.
[50]Appel O,Chiclana F,Carter J,et al.A consensus approach to the sentiment analysis problem driven by support-based IOWA majority[J].International Journal of Intelligent Systems,2017,32(9):947-965.
[51]Tang Xiao-bo,Lan Yu-ting.Sentiment analysis of microblog product reviews based on feature ontology[J].Library and Information Service,2016,60(16):121-127.(in Chinese)
[3]李寿山,李逸薇,黄居仁,等.基于双语信息和标签传播算法的中文情感词典构建方法[J].中文信息学报,2013,27(6):75-81.
[4]阳爱民,林江豪,周咏梅.中文文本情感词典构建方法[J].计算机科学与探索,2013,7(11):1033-1039.
[6]王志涛,於志文,郭斌,等.基于词典和规则集的中文微博情感分析[J].计算机工程与应用,2015,51(8):218-225.
[7]周杰.基于情感词典与句型分类的中文微博情感分析研究[D].银川:宁夏大学,2016.
[8]张克亮,黄金柱,曹蓉,等.基于HNC语境框架和情感词典的文本情感倾向分析[J].山东大学学报(理学版),2016,51(7):51-58.
[9]周哲,商琳.一种基于动态词典和三支决策的情感分析方法[J].山东大学学报(工学版),2015,45(1):19-23.
[10]栗雨晴,礼欣,韩煦,等.基于双语词典的微博多类情感分析方法[J].电子学报,2016,44(9):2068-2073.
[13]唐晓波,严承希.基于旋进原则和支持向量机的文本情感分析研究[J].情报理论与实践,2013,36(1):98-103.
[16]韩开旭.基于支持向量机的文本情感分析研究[D].大庆:东北石油大学,2014.
[24]梁斌,刘全,徐进,等.基于多注意力卷积神经网络的特定目标情感分析[J].计算机研究与发展,2017,54(8):1724-1735.
[25]刘金硕,张智.一种基于联合深度神经网络的食品安全信息情感分类模型[J].计算机科学,2016,43(12):277-280.
[26]谢铁,郑啸,张雷,等.基于并行化递归神经网络的中文短文本情感分类[J].计算机应用与软件,2017,34(3):205-211.
[28]曹宇慧.基于深度学习的文本情感分析研究[D].哈尔滨:哈尔滨工业大学硕士论文,2016.
[29]朱少杰.基于深度学习的文本情感分类研究[D].哈尔滨:哈尔滨工业大学硕士论文,2014.
[30]周瑛,刘越,蔡俊.基于注意力机制的微博情感分析[J].情报理论与实践,2018,41(3):89-94.
[33]梁军,柴玉梅,原慧斌,等.基于极性转移和LSTM递归网络的情感分析[J].中文信息学报,2015,29(5):152-159.
[34]田竹.基于深度特征提取的文本情感极性分类研究[D].威海:山东大学,2017.
[36]杜昌顺,黄磊.分段卷积神经网络在文本情感分析中的应用[J].计算机工程与科学,2017,39(1):173-179.
[38]张志华.基于深度学习的情感词向量及文本情感分析的研究[D].上海:华东师范大学,2016.
[39]吴冬茵,桂林,陈钊,等.基于深度表示学习和高斯过程迁移学习的情感分析方法[J].中文信息学报,2017,31(1):169-176.
[40]高歌,罗珺玫,王宇.基于HNC理论的文本情感倾向性分析[J].数据分析与知识发现,2017,1(8):85-91.
[42]杨立公,汤世平,朱俭.基于马尔科夫逻辑网的句子情感分析方法[J].北京理工大学学报,2013,33(6):600-604.
[43]赵妍妍,秦兵,车万翔,等.基于句法路径的情感评价单元识别[J].软件学报,2011,22(5):887-898.
[44]樊娜,安毅生,李慧贤.基于K-近邻算法的文本情感分析方法研究[J].计算机工程与设计,2012,33(3):1160-1164.
[51]唐晓波,兰玉婷.基于特征本体的微博产品评论情感分析[J].图书情报工作,2016,60(16):121-127.
[2]Whissell C.Using the revised dictionary of affect in language to quantity the emotional undertones of samples of natural language[J].Psychological Reports,2009,105(2):509-521.
[3]Li Shou-shan,Li Yi-wei,Huang Ju-ren,et al.Construction of Chinese sentiment lexicon using bilingual information and label propagation algorithm[J].Journal of Chinese Information Processing,2013,27(6):75-81.(in Chinese)
[4]Yang Ai-min,Lin Jiang-hao,Zhou Yong-mei.Method on building Chinese text sentiment lexicon[J].Journal of Frontiers of Computer Science and Technology,2013,7(11):1033-1039.(in Chinese)
[5]Rao Yang-hui,Lei Jing-sheng,Liu Wen-yin,et al.Building emotional dictionary for sentiment analysis of online news[J].World Wide Web-internet&Web Information Systems,2014,17(4):723-742.
[6]Wang Zhi-tao,Yu Zhi-wen,Guo Bin,et al.Sentiment analysis of Chinese micro blog based on lexicon and rule set[J].Computer Engineering and Applications,2015,51(8):218-225.(in Chinese)
[7]Zhou Jie.Research of sentiment analysis for the Chinese microblog based on emotional dictionary and sentence classification[D].Yinchuan:Ningxia University,2016.(in Chinese)
[8]Zhang Ke-liang,Huang Jin-zhu,Cao Rong,et al.Text sentimental orientation analysis based on HNC contextual framework and sentimental dictionaries[J].Journal of Shandong University(Natural Science),2016,51(7):51-58.(in Chinese)
[9]Zhou Zhe,Shang Lin.A sentiment analysis method based on dynamic lexicon and three-way decision[J].Journal of Shandong University(Engineering Science),2015,45(1):19-23.(in Chinese)
[10]Li Yu-qing,Li Xin,Han Xu,et al.A bilingual lexicon-based multi-class semantic orientation analysis for microblogs[J].Acta Electronica Sinica,2016,44(9):2068-2073.(in Chinese)
[11]Vilares D,Alonso M A,Gmez-Rodrguez C.Supervised sentiment analysis in multilingual environments[J].Information Processing and Management,2017,53(3):595-607.
[12]Sharma A,Dey S.A boosted SVM based ensemble classifier for sentiment analysis of online reviews[J].ACM SIGAPPApplied Computing Review,2013,13(4):43-52.
[13]Tang Xiao-bo,Yan Cheng-xi.A sentimental classification model based on SPIPRO principle and support vector machine[J].Information Studies:Theory&Application,2013,36(1):98-103.(in Chinese)
[14]Manek A S,Shenoy P D,Mohan M C,et al.Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier[J].World Wide Web-internet&Web Information Systems,2017,20(2):135-154.
[15]Abbasi A,France S,Zhu Zhang,et al.Selecting Attributes for sentiment classification using feature relation networks[J].IEEE Transactions on Knowledge&Data Engineering,2011,23(3):447-462.
[16]Han Kai-xu.Text sentiment analysis based on SVM[D].Daqing:Northeast Petroleum University,2014.(in Chinese)
[17]Shi Han-xiao,Zhan Wen-ping,Li Xiao-jun.A supervised fine-grained sentiment analysis system for online reviews[J].Intelligent Automation&Soft Computing,2015,21(4):589-605.
[18]Deng Zhi-hong,Luo Kun-hu,Yu Hong-liang.A study of supervised term weighting scheme for sentiment analysis[J].Expert Systems with Applications,2014,41(7):3506-3513.
[19]Perikos I,Hatzilygeroudis I.Recognizing emotions in text using ensemble of classifiers[J].Engineering Applications of Artificial Intelligence,2016,51:191-201.
[20]Tripathy A,Agrawal A,Rath S K.Classification of sentiment reviews using n-gram machine learning approach[J].Expert Systems with Applications,2016,57:117-126.
[21]Giatsoglou M,Vozalis M G,Diamantaras K I,et al.Sentiment analysis leveraging emotions and word embeddings[J].Expert Systems with Applications,2017,69:214-224.
[22]Tang Du-yu,Wei Fu-ru,Qin Bing,et al.Sentiment embeddings with applications to sentiment analysis[J].IEEETransactions on Knowledge&Data Engineering,2016,28(2):496-509.
[23]Fernández-Gavilanes M,álvarez-López T,Juncal-Martínez J,et al.Unsupervised method for sentiment analysis in online texts[J].Expert Systems with Applications,2016,58:57-75.
[24]Liang Bin,Liu Quan,Xu Jin,et al.Aspect-based sentiment analysis based on multi-attention CNN[J].Journal of Computer Research and Development,2017,54(8):1724-1735.(in Chinese)
[25]Liu Jin-shuo,Zhang Zhi.Sentiment analysis on food safety news using joint deep neural network model[J].Computer Science,2016,43(12):277-280.(in Chinese)
[26]Xie Tie,Zheng Xiao,Zhang Lei,et al.Sentiment classification of Chinese short text based on parallelized recursive neural network[J].Computer Applications and Software,2017,34(3):205-211.(in Chinese)
[27]Sun Xiao,Li Cheng-cheng,Ren Fu-ji.Sentiment analysis for Chinese microblog based on deep neural networks with convolutional extension features[J].Neurocomputing,2016,210(3):227-236.
[28]Cao Yu-hui.The research on text sentiment analysis based on deep learning[D].Harbin:Harbin Institute of Technology,2016.(in Chinese)
[29]Zhu Shao-jie.Research on text sentiment classification based on deep learning[D].Harbin:Harbin Institute of Technology,2014.(in Chinese)
[30]Zhou Ying,Liu Yue,Cai Jun.Sentiment analysis of microblogs based on attention mechanism[J].Information Studies:Theory&Application,2018,41(3):89-94.(in Chinese)
[31]Hu Fei,Li Li,Zhang Zi-li,et al.Emphasizing essential words for sentiment classification based on recurrent neural networks[J].Journal of Computer Science and Technology,2017,32(4):785-795.
[32]Ma Yu-kun,Peng Hai-yun,Khan T,et al.Sentic LSTM:Ahybrid network for targeted aspect-based sentiment analysis[J].Cognitive Computation,2018,10(4):639-650.
[33]Liang Jun,Chai Yu-mei,Yuan Hui-bin,et al.Polarity shifting and LSTM based recursive networks for sentiment analysis[J].Journal of Chinese Information Processing,2015,29(5):152-159.(in Chinese)
[34]Tian Zhu.Research on sentiment analysis based on deep feature representation[D].Weihai:Shandong University,2017.(in Chinese)
[35]Zhou Yu,Xu Rui-feng,Gui Lin.A sequence level latent topic modeling method for sentiment analysis via CNN based diversified restrict Boltzmann machine[C]∥Proc of International Conference on Machine Learning and Cybernetics,2016:356-361.
[36]Du Chang-shun,Huang Lei.Sentiment analysis with piecewise convolution neural network[J].Computer Engineering&Science,2017,39(1):173-179.(in Chinese)
[37]Xu Xiang-yu,Pan Jin-shan,Zhang Yu-jin,et al.Motion blur kernel estimation via deep learning[J].IEEE Transactions on Image Processing,2018,27(1):194-205.
[38]Zhang Zhi-hua.Deep learning based sentiment word vector towards sentiment analysis[D].Weihai:East China Normal University,2016.(in Chinese)
[39]Wu Dong-yin,Gui Lin,Chen Zhao,et al.Sentiment analysis based on deep representation learning and gaussian processes transfer learning[J].Journal of Chinese Information Processing,2017,31(1):169-176.(in Chinese)
[40]Gao Ge,Luo Jun-mei,Wang Yu.Analyzing textual sentiment based on HNC theory[J].Data Analysis and Knowledge Discovery,2017,1(8):85-91.(in Chinese)
[41]Pandey A C,Rajpoot D S,Saraswat M.Twitter sentiment analysis using hybrid cuckoo search method[J].Information Processing and Management,2017,53(4):764-779.
[42]Yang Li-gong,Tang Shi-ping,Zhu Jian.A markov logic network based sentence sentimental analysis method[J].Transactions of Beijing Institute of Technology,2013,33(6):600-604.(in Chinese)
[43]Zhao Yan-yan,Qin Bin,Che Wan-xiang,et al.Appraisal expression recognition based on syntactic path[J].Journal of Software,2011,22(5):887-898.(in Chinese)
[44]Fan Na,An Yi-sheng,Li Hui-xian.Research on analyzing sentiment of texts based on k-nearest neighbor algorithm[J].Computer Engineering and Design,2012,33(3):1160-1164.(in Chinese)
[45]Grandi U,Loreggia A,Rossi F,et al.A Borda count for collective sentiment analysis[J].Annals of Mathematics&Artificial Intelligence,2016,77(3-4):281-302.
[46]Eliacik A B,Erdogan N.Influential user weighted sentiment analysis on topic based microblogging community[J].Expert Systems with Applications,2018,92:403-418.
[47]Dragoni M,Petrucci G.A fuzzy-based strategy for multi-domain sentiment analysis[J].International Journal of Approximate Reasoning,2018,93(10):59-73.
[48]Yousefpour A,Ibrahim R,Hamed H N A.Ordinal-based and frequency-based integration of feature selection methods for sentiment analysis[J].Expert Systems with Applications,2017,75:80-93.
[49]Mukhtar N,Khan M A,Chiragh N.Effective use of evaluation measures for the validation of best classifier in Urdu sentiment analysis[J].Cognitive Computation,2017,9(4):446-456.
[50]Appel O,Chiclana F,Carter J,et al.A consensus approach to the sentiment analysis problem driven by support-based IOWA majority[J].International Journal of Intelligent Systems,2017,32(9):947-965.
[51]Tang Xiao-bo,Lan Yu-ting.Sentiment analysis of microblog product reviews based on feature ontology[J].Library and Information Service,2016,60(16):121-127.(in Chinese)
[3]李寿山,李逸薇,黄居仁,等.基于双语信息和标签传播算法的中文情感词典构建方法[J].中文信息学报,2013,27(6):75-81.
[4]阳爱民,林江豪,周咏梅.中文文本情感词典构建方法[J].计算机科学与探索,2013,7(11):1033-1039.
[6]王志涛,於志文,郭斌,等.基于词典和规则集的中文微博情感分析[J].计算机工程与应用,2015,51(8):218-225.
[7]周杰.基于情感词典与句型分类的中文微博情感分析研究[D].银川:宁夏大学,2016.
[8]张克亮,黄金柱,曹蓉,等.基于HNC语境框架和情感词典的文本情感倾向分析[J].山东大学学报(理学版),2016,51(7):51-58.
[9]周哲,商琳.一种基于动态词典和三支决策的情感分析方法[J].山东大学学报(工学版),2015,45(1):19-23.
[10]栗雨晴,礼欣,韩煦,等.基于双语词典的微博多类情感分析方法[J].电子学报,2016,44(9):2068-2073.
[13]唐晓波,严承希.基于旋进原则和支持向量机的文本情感分析研究[J].情报理论与实践,2013,36(1):98-103.
[16]韩开旭.基于支持向量机的文本情感分析研究[D].大庆:东北石油大学,2014.
[24]梁斌,刘全,徐进,等.基于多注意力卷积神经网络的特定目标情感分析[J].计算机研究与发展,2017,54(8):1724-1735.
[25]刘金硕,张智.一种基于联合深度神经网络的食品安全信息情感分类模型[J].计算机科学,2016,43(12):277-280.
[26]谢铁,郑啸,张雷,等.基于并行化递归神经网络的中文短文本情感分类[J].计算机应用与软件,2017,34(3):205-211.
[28]曹宇慧.基于深度学习的文本情感分析研究[D].哈尔滨:哈尔滨工业大学硕士论文,2016.
[29]朱少杰.基于深度学习的文本情感分类研究[D].哈尔滨:哈尔滨工业大学硕士论文,2014.
[30]周瑛,刘越,蔡俊.基于注意力机制的微博情感分析[J].情报理论与实践,2018,41(3):89-94.
[33]梁军,柴玉梅,原慧斌,等.基于极性转移和LSTM递归网络的情感分析[J].中文信息学报,2015,29(5):152-159.
[34]田竹.基于深度特征提取的文本情感极性分类研究[D].威海:山东大学,2017.
[36]杜昌顺,黄磊.分段卷积神经网络在文本情感分析中的应用[J].计算机工程与科学,2017,39(1):173-179.
[38]张志华.基于深度学习的情感词向量及文本情感分析的研究[D].上海:华东师范大学,2016.
[39]吴冬茵,桂林,陈钊,等.基于深度表示学习和高斯过程迁移学习的情感分析方法[J].中文信息学报,2017,31(1):169-176.
[40]高歌,罗珺玫,王宇.基于HNC理论的文本情感倾向性分析[J].数据分析与知识发现,2017,1(8):85-91.
[42]杨立公,汤世平,朱俭.基于马尔科夫逻辑网的句子情感分析方法[J].北京理工大学学报,2013,33(6):600-604.
[43]赵妍妍,秦兵,车万翔,等.基于句法路径的情感评价单元识别[J].软件学报,2011,22(5):887-898.
[44]樊娜,安毅生,李慧贤.基于K-近邻算法的文本情感分析方法研究[J].计算机工程与设计,2012,33(3):1160-1164.
[51]唐晓波,兰玉婷.基于特征本体的微博产品评论情感分析[J].图书情报工作,2016,60(16):121-127.
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