Please wait a minute...
Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (6): 1-13    DOI: 10.11925/infotech.2096-3467.2021.0040
Current Issue | Archive | Adv Search |
Review of Methods and Applications of Text Sentiment Analysis
Zhong Jiawa1,2,Liu Wei1(),Wang Sili1,Yang Heng1
1Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
2School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China
Download: PDF (1352 KB)   HTML ( 52
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective] This paper reviews literature on text sentiment analysis, aiming to summarize its technical development trends and applications. [Coverage] We searched relevant literature from the Web of Science Core Collection and CNKI database on the concepts, methods and techniques of sentiment analysis. A total of 69 papers were retrieved from 2011 to 2020 and then analyzed. [Methods] We summarized the main models and applications of text sentiment analysis from the dimensions of time and theme. We also discussed the fields needs to be improved. [Results] There were mainly three methods for text sentiment analysis, which were based on sentiment lexicon and rules, machine learning, as well as deep learning. Each method has advantages and disadvantages. The methods based on multi-strategy hybrid became more popular in recent years. [Limitations] We reviewed previous literature on text sentiment analysis from the perspective of macro-technical methods. More research is needed to compare and elaborate the technical details of sentiment analysis algorithms. [Conclusions] The development of artificial intelligence technology (big data and deep learning) will further improve text sentiment analysis, and benefit business decision making applications.

Key wordsSentiment Analysis      Sentiment Lexicon      Machine Learning      Deep Learning     
Received: 13 January 2021      Published: 19 March 2021
ZTFLH:  TP391  
Fund:National Key Research and Development Project of China(2018YFC1509007);Young Scholars in the West Program Class A of the Light of the West(Y9AX011001)
Corresponding Authors: Liu Wei     E-mail: liuw@llas.ac.cn

Cite this article:

Zhong Jiawa,Liu Wei,Wang Sili,Yang Heng. Review of Methods and Applications of Text Sentiment Analysis. Data Analysis and Knowledge Discovery, 2021, 5(6): 1-13.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.0040     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2021/V5/I6/1

Annual Number of Papers from 2011 to 2020
Terms Co-occurrence Network of Sentiment Analysis
方法 英文文献 中文文献
基于情感词典与规则的方法 情感词典、语义相似度、关联规则等 领域情感词典、依存句法分析、语义规则、语义相似度、本体等
基于机器学习的方法 支持向量机、朴素贝叶斯、逻辑回归、LDA主题模型、随机森林、决策树、遗传算法、集成学习、最大熵等 支持向量机、LDA主题模型、条件随机场、朴素贝叶斯、协同过滤、集成学习、随机森林、最大熵、K-Means等
基于深度学习的方法 卷积神经网络、长短期记忆网络、注意力机制、循环神经网络、双向长短期记忆网络等 卷积神经网络、注意力机制、长短期记忆网络、双向长短期记忆网络、循环神经网络、递归神经网络、生成对抗网络等
Models of Sentiment Analysis
应用 英文文献 中文文献
社交媒体 Twitter、微博、Facebook、公众意见、预测、危机、政治、健康、疾病、讽刺检测 微博、Twitter、舆情分析、预测、观点分析
在线评论 商品评论、消费者评论、用户评论、电影评论、酒店评论、旅游评论 商品评论、用户评论、电影评论、弹幕
商业投资 股票市场、股票价格、投资者情绪 股票预测、股票市场、投资者情绪、行为金融
其他 新闻文章、阿拉伯语、迁移学习、跨领域、跨语言 新闻、维吾尔语、新词发现、迁移学习、跨领域、多模态、跨语言
Applications in Sentiment Analysis
作者 基础词典 其他词典 规则 数据来源 情感极性分类效果
P/% R/% F1/%
董丽丽等[25] HowNet 网络情感词、未登录情感词、否定词、程度副词、关联词 ZOL中的笔记本电脑评论 75.44 81.21 78.22
Asghar等[26] SentiWordNet 表情符号、修饰语、否定词、领域术语 酒店评论数据 82.50 83.50 82.99
Han等[27] SentiWordNet 领域情感词 IMDB数据集 76.96 76.81 76.87
李晨等[28] HowNet、NTUSD、哈工大同义词词林 转折归总词、程度副词、否定词 新闻、博客和论坛数据 76.00 81.00 78.42
胡召亚等[29] 大连理工情感词汇本体库 表情符号 句型规则、句间关系规则 公开的微博情感分析语料 70.70 68.30 69.40
吴杰胜等[30] HowNet、NTUSD、大连理工情感词汇本体库 领域情感词、否定词、双重否定词、程度副词、关系连词、表情符 句型规则、句间关系规则 与“短视频整顿”话题相关的微博文本 82.10 82.70 83.40
王志涛等[31] HowNet、NTUSD 新词、修饰词表、表情符词表 句型规则、句间关系规则、表情符规则、词语多元组规则 新浪微博文本数据 68.30 67.10 67.70
The Comparison of Models Based on Sentiment Lexicons and Rules
作者 模型 算法特点 数据来源 情感极性分类效果
P/% R/% F1/%
谢丽星等[38] SVM 用层次结构,将情感分析过程分为两大策略、4种方法 新浪中的影视、名人和产品领域 67.28 - -
刘宝芹等[39] NB 建立三层树状情绪分类结构 不同话题的微博文本 70.60 65.30 67.80
Wawre等[40] NB 对于大规模训练集,朴素贝叶斯方法更好 IMDB数据集 66.77 62.00 64.29
Kaur等[42] KNN N-gram用于特征提取,特征提取与分类技术相结合 电子商务网站的评论 82.00 81.50 81.75
徐建忠等[43] SVM 设计特征向量,采用有监督的机器学习算法进行分类 航天事件相关的微博文本 80.30 78.50 79.40
李锐等[44] SVM 对词向量进行加权,解决文本特征稀疏的问题 公开的微博情感分析语料 89.35 89.35 89.35
Rathor等[45] SVM SVM的学习精度高 公开的Amazon评论数据集 81.20 - -
The Comparison of Models Based on Machine Learning
作者 模型 算法特点 数据来源 情感极性分类效果
P/% R/% F1/%
孙敏等[49] ATT+BGRU BGRU提取上下文信息,注意力机制调整特征权重 IMDB数据集 91.21 91.24 91.23
刘思琴等[52] BERT+ATT+BiLSTM BERT能获取包含上下文语义信息的词向量,注意力机制分配权重 SST二分类数据集 83.68 96.71 89.72
方英兰等[53] BERT+ATT+BiLSTM BERT模型可以获取更完整的文本语义特征 商品评价数据 93.48 93.73 93.60
曾子明等[54] ATT+BiLSTM 用双重注意力模型学习各级特征权重分布,从词级和句子级来分析整体文本情感 与“红黄蓝事件”有关的微博文本 97.79 97.01 97.39
苏小英等[55] CNN 双卷积层结构可以从任意长度语句中抽取特征 COAE2013和COAE 2014发布的标注数据 70.10 71.50 70.79
张英等[56] BiLSTM+RNN BiLSTM进行情感要素的抽取时效果更好 COAE2014发布的微博数据 89.80 - -
孙晓等[57] DBN 深度信念网络解决了文本特征稀疏的问题 COAE2014发布的数据集 79.45 81.00 79.55
Zeng等[58] PosATT +LSTM 同时考虑了上下文词和上下文位置关系 SemEval2014发布的餐厅数据集 79.40 - -
Heikal等[59] CNN+LSTM 不依赖特征提取,注重词向量的训练 ASTD数据集 - - 64.46
冯兴杰等[61] ATT+CNN 减少了人工干预和对情感词典的依赖 酒店评论语料(ChnSentiCorp) 87.27 87.81 87.19
The Comparison of Models Based on Deep Learning
作者 模型 算法特点 数据来源 情感极性分类效果
P/% R/% F1/%
Mukwazvure等[65] 情感词典+SVM 利用领域情感词典和意见规则可以获得更准确的情感标签 技术相关的评论文本 80.00 89.00 84.26
Rohini等[66] 情感词典+决策树 定义了领域特征实体的属性,有助于提取主观词 卡纳达语电影网站评论 78.00 79.00 78.50
Lu等[63] 情感词典+SVM 构建多部情感词典计算情感词的权值 《我不是药神》的豆瓣电影评论 69.80 - -
张凌等[67] 情感词典+SVM 领域负面词对领域微博识别更有效 健康主题的微博文本 74.10 71.00 72.40
李慧等[68] 新词词典+CNN 识别网络新词提高分词准确率,构建评论的特征矩阵 酒店评论语料(ChnSentiCorp) 84.50 85.90 85.20
何雪琴等[14] 情感词典+句法规则+CNN 由于旅游文本更冗长复杂,采用词典来挖掘句法规则,混合模型更有效 携程网上的旅游评论 94.30 94.40 94.00
Chen等[16] 情感词典+BiLSTM 多个情感词典融合,双层BiLSTM网络分类效果最好 PTT上的军事生活评论 - - 88.41
The Comparison of Models of Multi-Strategy Hybrid
方法 模型 应用场景 优点 缺点
基于情感词典与规则的方法 情感词典 股票市场[19] 自动生成领域情感词典;扩展词库提高了分类性能 扩展词库存在误报率;没有考虑标签的情感
情感词典+规则集 网络新闻[28] 考虑了上下文联系;结合新闻文本特点定义多种语义规则 没有消除词语歧义;篇章情感通过简单的加权获得
基于传统机器学习的方法 Naive Bayes 政治选举[9] 解决了零计数问题;朴素贝叶斯克服了词汇量不足问题 Unigram词典的可用性存在挑战;要创建单字格情感词典
SVM 微博文本[44] TF-IDF计算词汇权重;SVM提高了分类准确率 没有考虑文本相似度
LDA+协同过滤 商品评论[37] 扩展向量维度有利于解决数据稀疏问题,提高推荐精确度 词对提取准确率不高;属性面评分预测计算过程复杂
基于深度学习的方法 CNN+注意力机制 酒店评论[61] 减少对人工构造特征和情感词典的依赖 没有考虑图文信息
BiLSTM+注意力机制 公共安全[54] 关注文本分层结构;多层粒度分析更精确 直接剔除了非文本表情符号;粒度方面没有更加细化
BGRU+注意力机制 电影评论[49] 加快训练速度;有效获取上下文语义信息与相关联的特征 不适用于数据量过大的数据集;GRU的并行能力较弱
多策略混合的方法 情感词典+SVM 电影评论[63] 实现词典扩充;带情感标注的数据使模型训练更加准确 没有考虑文本间的语义规则以及句间规则
句法规则+CNN 旅游评论[14] 降低了文本复杂度和误分率;CNN降低了过拟合风险 数据集的正负极评论分布不均衡导致模型的AUC值较低
情感词典+BiLSTM 论坛评论[16] 扩展情感词典;BiLSTM网络和激活函数提高分类精确度 提取情感词、构建情感词典存在难度;没有考虑中性文本
The Advantages and Disadvantages of Techniques and Models in Applications
[1] 陈龙, 管子玉, 何金红, 等. 情感分类研究进展[J]. 计算机研究与发展, 2017,54(6):1150-1170.
[1] (Chen Long, Guan Ziyu, He Jinhong, et al. A Survey on Sentiment Classification[J]. Journal of Computer Research and Development, 2017,54(6):1150-1170.)
[2] Joshi M, Prajapati P, Shaikh A, et al. A Survey on Sentiment Analysis[J]. International Journal of Computer Applications, 2017,163(6):34-38.
[3] Liu B. Sentiment Analysis and Opinion Mining[M]. San Rafael, CA : Morgan & Claypool Publishers, 2012.
[4] 王科, 夏睿. 情感词典自动构建方法综述[J]. 自动化学报, 2016,42(4):495-511.
[4] (Wang Ke, Xia Rui. A Survey on Automatical Construction Methods of Sentiment Lexicons[J]. Acta Automatica Sinica, 2016,42(4):495-511.)
[5] 梅莉莉, 黄河燕, 周新宇, 等. 情感词典构建综述[J]. 中文信息学报, 2016,30(5):19-27.
[5] (Mei Lili, Huang Heyan, Zhou Xinyu, et al. A Survey on Sentiment Lexicon Construction[J]. Journal of Chinese Information Processing, 2016,30(5):19-27.)
[6] Zhang L, Wang S, Liu B. Deep Learning for Sentiment Analysis: A Survey[J]. Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery, 2018,8(4):e1253.
[7] Minaee S, Kalchbrenner N, Cambria E, et al. Deep Learning Based Text Classification: A Comprehensive Review[OL]. arXiv Preprint, arXiv: 2004.03705.
[8] Mikolov T, Chen K, Corrado G S, et al. Efficient Estimation of Word Representations in Vector Space[OL]. arXiv Preprint, arXiv: 1301.3781.
[9] Awwalu J, Bakar A A, Yaakub M R. Hybrid N-Gram Model Using Naive Bayes for Classification of Political Sentiments on Twitter[J]. Neural Computing and Applications, 2019,31(12):9207-9220.
doi: 10.1007/s00521-019-04248-z
[10] Balakrishnan V, Khan S, Arabnia H R. Improving Cyberbullying Detection Using Twitter Users’ Psychological Features and Machine Learning[J]. Computers & Security, 2020,90:101710.
doi: 10.1016/j.cose.2019.101710
[11] 任中杰, 张鹏, 兰月新, 等. 面向突发事件的网络用户画像情感分析——以天津“8·12”事故为例[J]. 情报杂志, 2019,38(11):126-133.
[11] (Ren Zhongjie, Zhang Peng, Lan Yuexin, et al. Emotional Tendency Prediction of Emergencies Based on the Portraits of Weibo Users——Taking “8·12” Accident in Tianjin as an Example[J]. Journal of Intelligence, 2019,38(11):126-133.)
[12] 陈盼, 钱宇星, 黄智生, 等. 微博“树洞”留言的负性情绪特征分析[J]. 中国心理卫生杂志, 2020,34(5):437-444.
[12] (Chen Pan, Qian Yuxing, Huang Zhisheng, et al. Negative Emotional Characteristics of Weibo “Tree Hole” Users[J]. Chinese Mental Health Journal, 2020,34(5):437-444.)
[13] 雷鸣, 朱明. 情感分析在电影推荐系统中的应用[J]. 计算机工程与应用, 2016,52(10):59-63,107.
[13] (Lei Ming, Zhu Ming. Applications of Sentiment Analysis in Movie Recommendation System[J]. Computer Engineering and Application, 2016,52(10):59-63, 107.)
[14] 何雪琴, 杨文忠, 吾守尔·斯拉木, 等. 融合句法规则和CNN的旅游评论情感分析[J]. 计算机工程与设计, 2019,40(11):3306-3312.
[14] (He Xueqin, Yang Wenzhong, Wushouer · Silamu, et al. Sentiment Analysis of Tourist Reviews Combined with Syntactic Rules and CNN[J]. Computer Engineering and Design, 2019,40(11):3306-3312.)
[15] Griffith J, Najand M, Shen J C. Emotions in the Stock Market[J]. Journal of Behavioral Finance, 2020,21(1):42-56.
doi: 10.1080/15427560.2019.1588275
[16] Chen L C, Lee C M, Chen M Y. Exploration of Social Media for Sentiment Analysis Using Deep Learning[J]. Soft Computing, 2020,24(11):8187-8197.
doi: 10.1007/s00500-019-04402-8
[17] Pan D H, Yuan J L, Li L, et al. Deep Neural Network-Based Classification Model for Sentiment Analysis[C]// Proceedings of the 6th International Conference on Behavioral, Economic and Socio-Cultural Computing. New York, USA: IEEE, 2019. DOI: 10.1109/BESC48373.2019.8963171.
[18] Joshi A, Bhattacharyya P, Ahire S. Sentiment Resources: Lexicons and Datasets[A]//Cambria E, Das D, Bandyopadhyay S, et al. A Practical Guide to Sentiment Analysis[M]. Cham: Springer International Publishing, 2017: 85-106.
[19] Deng S Y, Sinha A P, Zhao H M. Adapting Sentiment Lexicons to Domain-Specific Social Media Texts[J]. Decision Support Systems, 2017,94:65-76.
doi: 10.1016/j.dss.2016.11.001
[20] 赵妍妍, 秦兵, 石秋慧, 等. 大规模情感词典的构建及其在情感分类中的应用[J]. 中文信息学报, 2017,31(2):187-193.
[20] (Zhao Yanyan, Qin Bing, Shi Qiuhui, et al. Large-scale Sentiment Lexicon Collection and Its Application in Sentiment Classification[J]. Journal of Chinese Information Processing, 2017,31(2):187-193.)
[21] 李永帅, 王黎明, 柴玉梅, 等. 基于双向LSTM的动态情感词典构建方法研究[J]. 小型微型计算机系统, 2019,40(3):503-509.
[21] (Li Yongshuai, Wang Liming, Chai Yumei, et al. Research on Construction Method of Dynamic Sentiment Dictionary Based on Bidirectional LSTM[J]. Journal of Chinese Computer Systems, 2019,40(3):503-509.)
[22] 万琪, 于中华, 陈黎, 等. 利用新词探测提高中文微博的情感表达抽取[J]. 中国科学技术大学学报, 2017,47(1):63-69.
[22] (Wan Qi, Yu Zhonghua, Chen Li, et al. Improving Emotion Expression Extraction in Chinese Microblogs via New Words Detection[J]. Journal of University of Science and Technology of China, 2017,47(1):63-69.)
[23] Ahmed M, Chen Q, Li Z H. Constructing Domain-Dependent Sentiment Dictionary for Sentiment Analysis[J]. Neural Computing and Applications, 2020,32(18):14719-14732.
doi: 10.1007/s00521-020-04824-8
[24] Taboada M, Brooke J, Tofiloski M, et al. Lexicon-Based Methods for Sentiment Analysis[J]. Computational Linguistics, 2011,37(2):267-307.
doi: 10.1162/COLI_a_00049
[25] 董丽丽, 赵繁荣, 张翔. 基于领域本体、情感词典的商品评论倾向性分析[J]. 计算机应用与软件, 2014,31(12):104-108, 194.
[25] (Dong Lili, Zhao Fanrong, Zhang Xiang. Analysing Propensity of Product Reviews Based on Domain Ontology and Sentiment Lexicon[J]. Computer Applications and Software, 2014,31(12):104-108, 194.)
[26] Asghar M Z, Khan A, Ahmad S, et al. Lexicon-Enhanced Sentiment Analysis Framework Using Rule-Based Classification Scheme[J]. PLoS One, 2017,12(2):e0171649.
doi: 10.1371/journal.pone.0171649
[27] Han H Y, Zhang J P, Yang J, et al. Generate Domain-Specific Sentiment Lexicon for Review Sentiment Analysis[J]. Multimedia Tools and Applications, 2018,77(16):21265-21280.
doi: 10.1007/s11042-017-5529-5
[28] 李晨, 朱世伟, 魏墨济, 等. 基于词典与规则的新闻文本情感倾向性分析[J]. 山东科学, 2017,30(1):115-121.
[28] (Li Chen, Zhu Shiwei, Wei Moji, et al. Lexicon and Rules Based News Text Sentiment Analysis[J]. Shandong Science, 2017,30(1):115-121.)
[29] 胡召亚, 张顺香. 基于关键句提取的中文微博情感计算[J]. 阜阳师范学院学报(自然科学版), 2019,36(3):92-96.
[29] (Hu Zhaoya, Zhang Shunxiang. Sentiment Calculation of Chinese Microblog Based on Key Sentences Extraction[J]. Journal of Fuyang Normal University (Natural Science), 2019,36(3):92-96.)
[30] 吴杰胜, 陆奎. 基于多部情感词典和规则集的中文微博情感分析研究[J]. 计算机应用与软件, 2019,36(9):93-99.
[30] (Wu Jiesheng, Lu Kui. Chinese Weibo Sentiment Analysis Based on Multiple Sentiment Lexicons and Rule Sets[J]. Computer Applications and Software, 2019,36(9):93-99.)
[31] 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.
[31] (Wang Zhitao, Yu Zhiwen, Guo Bin, et al. Sentiment Analysis of Chinese Micro Blog Based on Lexicon and Rule Set[J]. Computer Engineering and Application, 2015,51(8):218-225.)
[32] Neethu M S, Rajasree R. Sentiment Analysis in Twitter Using Machine Learning Techniques[C]// Proceedings of the 4th International Conference on Computing, Communications and Networking Technologies. DOI: 10.1109/ICCCNT.2013.6726818.
[33] 刘丽, 岳亚伟. 面向高校学生微博的跨粒度情感分析[J]. 计算机应用研究, 2019,36(6):1618-1622.
[33] (Liu Li, Yue Yawei. Cross-Grained Sentiment Analysis Oriented to College Student Microblog[J]. Application Research of Computers, 2019,36(6):1618-1622.)
[34] 唐莉, 刘臣. 基于CRF和HITS算法的特征情感对提取[J]. 计算机技术与发展, 2019,29(7):71-75.
[34] (Tang Li, Liu Chen. Extraction of Feature and Sentiment Word Pair Based on Conditional Random Fields and HITS Algorithm[J]. Computer Technology and Development, 2019,29(7):71-75.)
[35] Bandhakavi A, Wiratunga N, Padmanabhan D, et al. Lexicon Based Feature Extraction for Emotion Text Classification[J]. Pattern Recognition Letters, 2017,93:133-142.
doi: 10.1016/j.patrec.2016.12.009
[36] 杨莉, 王敏, 程宇. 基于LDA和XGBoost模型的环境公共服务微博情感分析[J]. 南京邮电大学学报(社会科学版), 2019,21(6):23-39.
[36] (Yang Li, Wang Min, Cheng Yu. Microblog Sentiment Analysis of Jiangsu Environmental Public Service Based on LDA and XGBoost Models[J]. Journal of Nanjing University of Posts and Telecommunications (Social Science), 2019,21(6):23-39.)
[37] 彭敏, 席俊杰, 代心媛, 等. 基于情感分析和LDA主题模型的协同过滤推荐算法[J]. 中文信息学报, 2017,31(2):194-203.
[37] (Peng Min, Xi Junjie, Dai Xinyuan, et al. Collaborative Filtering Recommendation Based on Sentiment Analysis and LDA Topic Model[J]. Journal of Chinese Information Processing, 2017,31(2):194-203.)
[38] 谢丽星, 周明, 孙茂松. 基于层次结构的多策略中文微博情感分析和特征抽取[J]. 中文信息学报, 2012,26(1):73-84.
[38] (Xie Lixing, Zhou Ming, Sun Maosong. Hierarchical Structure Based Hybrid Approach to Sentiment Analysis of Chinese Micro Blog and Its Feature Extraction[J]. Journal of Chinese Information Processing, 2012,26(1):73-83.)
[39] 刘宝芹, 牛耘. 多层次中文微博情绪分析[J]. 计算机技术与发展, 2015,25(11):23-26.
[39] (Liu Baoqin, Niu Yun. Multi-Hierarchy Emotion Analysis of Chinese Microblog[J]. Computer Technology and Development, 2015,25(11):23-26.)
[40] Wawre S V, Deshmukh S N. Sentiment Classification Using Machine Learning Techniques[J]. International Journal of Science and Research, 2016,5(4):819-821.
[41] Huq M R, Ali A, Rahman A. Sentiment Analysis on Twitter Data Using KNN and SVM[J]. International Journal of Advanced Computer Ence and Applications, 2017,8(6):19-25.
[42] Kaur S, Sikka G, Awasthi L K. Sentiment Analysis Approach Based on N-Gram and KNN Classifier[C]// Proceedings of the 1st International Conference on Secure Cyber Computing and Communications. 2018: 13-16.
[43] 徐建忠, 朱俊, 赵瑞, 等. 基于SVM算法的航天微博情感分析[J]. 信息安全研究, 2017,3(12):1129-1133.
[43] (Xu Jianzhong, Zhu Jun, Zhao Rui, et al. Sentiment Analysis of Aerospace Microblog Using SVM[J]. Journal of Information Security Research, 2017,3(12):1129-1133.)
[44] 李锐, 张谦, 刘嘉勇. 基于加权Word2vec的微博情感分析[J]. 通信技术, 2017,50(3):502-506.
[44] (Li Rui, Zhang Qian, Liu Jiayong. Microblog Sentiment Analysis Based on Weighted Word2vec[J]. Communications Technology, 2017,50(3):502-506.)
[45] Rathor A S, Agarwal A, Dimri P. Comparative Study of Machine Learning Approaches for Amazon Reviews[J]. Procedia Computer Science, 2018,132:1552-1561.
doi: 10.1016/j.procs.2018.05.119
[46] Yadav A, Vishwakarma D K. Sentiment Analysis Using Deep Learning Architectures: A Review[J]. Artificial Intelligence Review, 2020,53(6):4335-4385.
doi: 10.1007/s10462-019-09794-5
[47] Krizhevsky A, Sutskever I, Hinton G E. ImageNet Classification with Deep Convolutional Neural Networks[J]. Communication of the ACM, 2017,60(6):84-90.
doi: 10.1145/3065386
[48] Pennington J, Socher R, Manning C. GloVe: Global Vectors for Word Representation[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. 2014: 1532-1543.
[49] 孙敏, 李旸, 庄正飞, 等. 基于BGRU和自注意力机制的情感分析[J]. 江汉大学学报(自然科学版), 2020,48(4):80-89.
[49] (Sun Min, Li Yang, Zhuang Zhengfei, et al. Sentiment Analysis Based on BGRU and Self-Attention Mechanism[J]. Journal of Jianghan University(Natural Science Edition), 2020,48(4):80-89.)
[50] Peters M, Neumann M, Iyyer M, et al. Deep Contextualized Word Representations[OL]. arXiv Preprint, arXiv: 1802.05365.
[51] Devlin J, Chang M W, Lee K, et al. BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding[OL]. arXiv Preprint, arXiv: 1810.04805.
[52] 刘思琴, 冯胥睿瑞. 基于BERT的文本情感分析[J]. 信息安全研究, 2020,6(3):220-227.
[52] (Liu Siqin, Feng Xuruirui. Text Sentiment Analysis Based on BERT[J]. Journal of Information Security Research, 2020,6(3):220-227.)
[53] 方英兰, 孙吉祥, 韩兵. 基于BERT的文本情感分析方法的研究[J]. 信息技术与信息化, 2020 (2):108-111.
[53] (Fang Yinglan, Sun Jixiang, Han Bing. Research on Text Sentiment Analysis Method Based on BERT[J]. Information Technology and Informatization, 2020 (2):108-111.)
[54] 曾子明, 万品玉. 基于双层注意力和Bi-LSTM的公共安全事件微博情感分析[J]. 情报科学, 2019,37(6):23-29.
[54] (Zeng Ziming, Wan Pinyu. Sentiment Analysis of Public Safety Events in Micro-blog Based on Double-layered Attention and Bi-LSTM[J]. Information Science, 2019,37(6):23-29.)
[55] 苏小英, 孟环建. 基于神经网络的微博情感分析[J]. 计算机技术与发展, 2015,25(12):161-164,168.
[55] (Su Xiaoying, Meng Huanjian. Sentiment Analysis of Micro-blog Based on Neural Networks[J]. Computer Technology and Development, 2015,25(12):161-164, 168.)
[56] 张英, 郑秋生. 基于循环神经网络的互联网短文本情感要素抽取[J]. 中原工学院学报, 2016,27(6):82-86.
[56] (Zhang Ying, Zheng Qiusheng. Sentiment Classification of the Short Texts on Internet Based on Convolutional Neural Networks[J]. Journal of Zhongyuan University of Technology, 2016,27(6):82-86.)
[57] 孙晓, 彭晓琪, 胡敏, 等. 基于多维扩展特征与深度学习的微博短文本情感分析[J]. 电子与信息学报, 2017,39(9):2048-2055.
[57] (Sun Xiao, Peng Xiaoqi, Hu Min, et al. Extended Multi-Modality Features and Deep Learning Based Microblog Short Text Sentiment Analysis[J]. Journal of Electronics & Information Technology, 2017,39(9):2048-2055.)
[58] Zeng J F, Ma X, Zhou K. Enhancing Attention-Based LSTM with Position Context for Aspect-Level Sentiment Classification[J]. IEEE Access, 2019,7:20462-20471.
doi: 10.1109/ACCESS.2019.2893806
[59] Heikal M, Torki M, El-Makky N. Sentiment Analysis of Arabic Tweets Using Deep Learning[C]// Proceedings of the 4th Annual International Conference on Arabic Computational Linguistics. 2018: 114-122.
[60] 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.
[60] (Du Changshun, Huang Lei. Sentiment Analysis with Piecewise Convolution Neural Network[J]. Computer Engineering and Science, 2017,39(1):173-179.)
[61] 冯兴杰, 张志伟, 史金钏. 基于卷积神经网络和注意力模型的文本情感分析[J]. 计算机应用研究, 2018,35(5):1434-1436.
[61] (Feng Xingjie, Zhang Zhiwei, Shi Jinchuan. Text Sentiment Analysis Based on Convolutional Neural Networks and Attention Model[J]. Application Research of Computers, 2018,35(5):1434-1436.)
[62] 陈珂, 谢博, 朱兴统. 基于情感词典和Transformer模型的情感分析算法研究[J]. 南京邮电大学学报(自然科学版), 2020,40(1):55-62.
[62] (Chen Ke, Xie Bo, Zhu Xingtong. Sentiment Analysis Method Based on Sentiment Lexicon and Transformer[J]. Journal of Nanjing University of Posts and Telecommunications(Natural Science Edition), 2020,40(1):55-62.)
[63] Lu K, Wu J S. Sentiment Analysis of Film Review Texts Based on Sentiment Dictionary and SVM[C]// Proceedings of the 3rd International Conference on Innovation in Artificial Intelligence. 2019: 73-77.
[64] Fu X H, Liu W W, Xu Y Y, et al. Combine Hownet Lexicon to Train Phrase Recursive Autoencoder for Sentence-Level Sentiment Analysis[J]. Neurocomputing, 2017,241:18-27.
doi: 10.1016/j.neucom.2017.01.079
[65] Mukwazvure A, Supreethi K P. A Hybrid Approach to Sentiment Analysis of News Comments[C]// Proceedings of the 4th International Conference on Reliability, Infocom Technologies and Optimization. DOI: 10.1109/ICRITO.2015.7359282.
[66] Rohini V, Thomas M, Latha C A. Domain Based Sentiment Analysis in Regional Language-Kannada Using Machine Learning Algorithm[C]// Proceedings of IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology. 2016: 503-507.
[67] 张凌, 谭毅, 朱礼军, 等. 负面微博特征分析研究[J]. 情报理论与实践, 2019,42(7):132-137,170.
[67] (Zhang Ling, Tan Yi, Zhu Lijun, et al. Analyzing the Features of Negative Sentiment Microblog[J]. Information Studies: Theory & Application, 2019,42(7):132-137, 170.)
[68] 李慧, 柴亚青. 基于卷积神经网络的细粒度情感分析方法[J]. 数据分析与知识发现, 2019,3(1):95-103.
[68] (Li Hui, Chai Yaqing. Fine-Grained Sentiment Analysis Based on Convolutional Neural Network[J]. Data Analysis and Knowledge Discovery, 2019,3(1):95-103.)
[69] 张仰森, 郑佳, 黄改娟, 等. 基于双重注意力模型的微博情感分析方法[J]. 清华大学学报(自然科学版), 2018,58(2):122-130.
[69] (Zhang Yangsen, Zheng Jia, Huang Gaijuan, et al. Microblog Sentiment Analysis Method Based on a Double Attention Model[J]. Journal of Tsinghua University (Science and Technology), 2018,58(2):122-130.)
[1] Wang Hanxue,Cui Wenjuan,Zhou Yuanchun,Du Yi. Identifying Pathogens of Foodborne Diseases with Machine Learning[J]. 数据分析与知识发现, 2021, 5(9): 54-62.
[2] Chen Donghua,Zhao Hongmei,Shang Xiaopu,Zhang Runtong. Optimizing Large Hospital Operating Rooms with Data Analytics[J]. 数据分析与知识发现, 2021, 5(9): 115-128.
[3] Che Hongxin,Wang Tong,Wang Wei. Comparing Prediction Models for Prostate Cancer[J]. 数据分析与知识发现, 2021, 5(9): 107-114.
[4] Zhou Zeyu,Wang Hao,Zhao Zibo,Li Yueyan,Zhang Xiaoqin. Construction and Application of GCN Model for Text Classification with Associated Information[J]. 数据分析与知识发现, 2021, 5(9): 31-41.
[5] Su Qiang, Hou Xiaoli, Zou Ni. Predicting Surgical Infections Based on Machine Learning[J]. 数据分析与知识发现, 2021, 5(8): 65-75.
[6] Zhao Danning,Mu Dongmei,Bai Sen. Automatically Extracting Structural Elements of Sci-Tech Literature Abstracts Based on Deep Learning[J]. 数据分析与知识发现, 2021, 5(7): 70-80.
[7] Xu Yuemei, Wang Zihou, Wu Zixin. Predicting Stock Trends with CNN-BiLSTM Based Multi-Feature Integration Model[J]. 数据分析与知识发现, 2021, 5(7): 126-138.
[8] Huang Mingxuan,Jiang Caoqing,Lu Shoudong. Expanding Queries Based on Word Embedding and Expansion Terms[J]. 数据分析与知识发现, 2021, 5(6): 115-125.
[9] Cao Rui,Liao Bin,Li Min,Sun Ruina. Predicting Prices and Analyzing Features of Online Short-Term Rentals Based on XGBoost[J]. 数据分析与知识发现, 2021, 5(6): 51-65.
[10] Liu Tong,Liu Chen,Ni Weijian. A Semi-Supervised Sentiment Analysis Method for Chinese Based on Multi-Level Data Augmentation[J]. 数据分析与知识发现, 2021, 5(5): 51-58.
[11] Zhang Guobiao,Li Jie. Detecting Social Media Fake News with Semantic Consistency Between Multi-model Contents[J]. 数据分析与知识发现, 2021, 5(5): 21-29.
[12] Xiang Zhuoyuan,Liu Zhicong,Wu Yu. Adaptive Recommendation Model Based on User Behaviors[J]. 数据分析与知识发现, 2021, 5(4): 103-114.
[13] Wang Yuzhu,Xie Jun,Chen Bo,Xu Xinying. Multi-modal Sentiment Analysis Based on Cross-modal Context-aware Attention[J]. 数据分析与知识发现, 2021, 5(4): 49-59.
[14] Li Feifei,Wu Fan,Wang Zhongqing. Sentiment Analysis with Reviewer Types and Generative Adversarial Network[J]. 数据分析与知识发现, 2021, 5(4): 72-79.
[15] Hu Haotian,Ji Jinfeng,Wang Dongbo,Deng Sanhong. An Integrated Platform for Food Safety Incident Entities Based on Deep Learning[J]. 数据分析与知识发现, 2021, 5(3): 12-24.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn