Please wait a minute...
Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (5): 77-87    DOI: 10.11925/infotech.2096-3467.2017.1316
Orginal Article Current Issue | Archive | Adv Search |
Review of Online Sentiment Visualization Techniques
Sinan Yang1,Jian Xu1(),Pingping Ye2
1 School of Information Management, Sun Yat-Sen University, Guangzhou 510006, China
2 Shenzhen LEXIN Holdings Limited, Shenzhen 518000, China
Download: PDF(6510 KB)   HTML ( 1
Export: BibTeX | EndNote (RIS)      

[Objective] The paper reviews the main techniques for sentiment analysis of online reviews, and then discusses their major development trends. [Methods] First, we surveyed relevant scientific literature on sentiment analysis of web reviews published in recent years. Then, we summarized the characteristics of visualization methods and analyzed features of visualization tools. [Results] We could visualize the sentiment of web reviews from the perspectives of contents, space-time, and topics. The visualization tools include static, interactive and programming ones. [Conclusions] This paper reviews the major methods and tools for online contents visualization and indicates three major development trends. It could promote the progress of future research and new visualization tools.

Key wordsSentiment Visualization      Sentiment Analysis      Visualization Tools     
Received: 25 December 2017      Published: 20 June 2018

Cite this article:

Sinan Yang,Jian Xu,Pingping Ye. Review of Online Sentiment Visualization Techniques. Data Analysis and Knowledge Discovery, 2018, 2(5): 77-87.

URL:     OR

[1] 任磊, 杜一, 马帅, 等. 大数据可视分析综述[J]. 软件学报, 2014, 25(9): 1909-1936.
[1] (Ren Lei, Du Yi, Ma Shuai, et al.Visual Analytics Towards Big Data[J]. Journal of Software, 2014, 25(9): 1909-1936.)
[2] 杜嘉忠, 徐健, 刘颖. 网络商品评论的特征—情感词本体构建与情感分析方法研究[J]. 现代图书情报技术, 2014(5): 74-82.
[2] (Du Jiazhong, Xu Jian, Liu Ying.Research on Construction of Feature-Sentiment Ontology and Sentiment Analysis[J]. New Technology of Library and Information Service, 2014(5): 74-82.)
[3] 程翠琼, 徐健. 面向网络游记时间特征的情感分析模型[J]. 数据分析与知识发现, 2017, 1(2): 87-95.
[3] (Cheng Cuiqiong, Xu Jian.A Sentiment Analysis Model Based on Temporal Characteristics of Travel Blogs[J]. Data Analysis and Knowledge Discovery, 2017, 1(2): 87-95.)
[4] 李涵昱, 钱力, 周鹏飞. 面向商品评论文本的情感分析与挖掘[J]. 情报科学, 2017, 35(1): 51-55.
[4] (Li Hanyu, Qian Li, Zhou Pengfei.Sentiment Analysis and Mining of Product Reviews[J]. Information Science, 2017, 35(1): 51-55.)
[5] 郑飏飏, 徐健, 肖卓. 情感分析及可视化方法在网络视频弹幕数据分析中的应用[J]. 现代图书情报技术, 2015(11): 82-90.
[5] (Zheng Yangyang, Xu Jian, Xiao Zhuo.Utilization of Sentiment Analysis and Visualization in Online Video Bullet-screen Comments[J]. New Technology of Library and Information Service, 2015(11): 82-90.)
[6] 朱琳琳, 徐健.网络评论情感分析关键技术及应用研究[J]. 情报理论与实践, 2017, 40(1): 121-126.
[6] (Zhu Linlin, Xu Jian.Research on the Key Technologies and Applications of Sentimental Analysis in Network Review[J]. Information Studies: Theory & Application, 2017, 40(1): 121-126.)
[7] 杜贺, 於志文, 王志涛.微博情感可视化系统[J].中国科技论文, 2014, 9(10): 1144-1148.
[7] (Du He, Yu Zhiwen, Wang Zhitao.Visualization System of Microblog Sentiment[J]. China Science Paper, 2014, 9(10): 1144-1148.)
[8] Cao N, Cui W.Introduction to Text Visualization[M]. Atlantics Press, 2016: 41-48.
[9] Bag of Words [EB/OL]. [2017-12-13]..
[10] Cui W, Liu S, Tan L, et al.TextFlow: Towards Better Understanding of Evolving Topics in Text[J]. IEEE Transactions on Visualization & Computer Graphics, 2011, 17(12): 2412-2421.
[11] IN-SPIRETM Visual Document Analysis [EB/OL]. [2017-10- 31]. .
[12] Dave K, Lawrence S, Pennock D M.Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews[C]// Proceedings of International Conference on World Wide Web. ACM, 2003: 519-528.
[13] Hu M, Liu B.Mining and Summarizing Customer Reviews[C]// Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, Washington, USA. 2004: 168-177.
[14] Hao M, Rohrdandz C, Janetzko H, et al.Visual Sentiment Analysis on Twitter Data Streams[C]// Proceedings of the 2011 IEEE Conference on Visual Analytics Science and Technology. 2011: 277-278.
[15] Golder S A, Macy M W.Diurnal and Seasonal Mood Vary with Work, Sleep, and Daylength Across Diverse Cultures[J]. Science, 2011, 333(6051): 1878-1881.
[16] Wang F Y, Sallaberry A, Klein K, et al.SentiCompass: Interactive Visualization for Exploring and Comparing the Sentiments of Time-varying Twitter Data[C]// Proceedings of the Visualization Symposium. IEEE, 2015: 129-133.
[17] Xu P, Wu Y, Wei E, et al.Visual Analysis of Topic Competition on Social Media[J]. IEEE Transactions on Visualization & Computer Graphics, 2013, 19(12): 2012-2021.
[18] Zhao J, Gou L, Wang F, et al.PERAL: An Interactiv Visual Anlytic Tool for Understanding Personal Emotion Style Derived from Social Media[C]// Proceedings of the 2014 IEEE Conference on Visual Analytics Science and Technology. IEEE, 2015: 203-212.
[19] 赵琦, 张智雄, 孙坦.文本可视化及其主要技术方法研究[J]. 现代图书情报技术, 2008(8): 24-30.
[19] (Zhao Qi, Zhang Zhixiong, Sun Tan.A Research on the Methodological of Text Visualization[J]. New Technology of Library and Information Service, 2008(8): 24-30.)
[20] Tbleau [EB/OL]. [2017-10-31]..
[21] 郭传斌, 刘琦岩, 赵婧, 等.情报学视角下的文本可视化应用[J]. 情报工程, 2017, 3(4): 48-61.
[21] (Guo Chuanbin, Liu Qiyan, Zhao Jing, et al.Study on Text Visualization from the Information Science Perspective[J]. Technology Intelligence Engineering, 2017, 3(4): 48-61.)
[22] Sankey Diagram [EB/OL]. [2017-12-14]..
[23] BDP [EB/OL]. [2017-12-14]..
[24] The R Project for Statistical Computing [EB/OL]. [2017- 10-31]..
[25] Cao N, Lin Y R, Sun X, et al.Whisper: Tracing the Spatiotemporal Process of Information Diffusion in Real Time[J]. IEEE Transactions on Visualization & Computer Graphics, 2012, 18(12): 2649-2658.
[26] Mislove A, Lehmann S, Ahn Y-Y, et al. Pulse of the Nation: U.S. Mood Throughout the Day Inferred from Twitter [EB/OL]. [2017-10-31]. .
[27] Excel [EB/OL]. [2017-10-31]. .
[28] Wordle [EB/OL]. [2017-10-31]. .
[29] Visually Content Marketing for Brands [EB/OL]. [2017-10-31]. .
[30] Crossfilter [EB/OL]. [2017-10-31]..
[31] Prefuse [EB/OL]. [2017-10-31]. .
[32] D3 [EB/OL]. [2017-10-31]. .
[33] EChart[EB/OL].[2017-10-31].// .
[34] Google Chart [EB/OL]. [2017-10-31]..
[35] Weka3 Data Mining Software in Java [EB/OL]. [2017-10-31]. .
[36] Processing [EB/OL]. [2017-10-31]..
[37] Many Eyes [EB/OL]. [2017-10-31]. .
[38] iCharts[EB/OL].[2017-10-31]. .
[39] Cui W, Wu Y, Liu S, et al.Context-Preserving, Dynamic Word Cloud Visualization[J]. IEEE Computer Graphics & Applications, 2010, 30(6): 42-53.
[40] Hu M, Wongsuphasawat K, Stasko J.Visualizing Social Media Content with SentenTree[J]. IEEE Transactions on Visualization & Computer Graphics, 2017, 23(1): 621-630.
[41] Cao N, Lu L, Lin Y R, et al.SocialHelix: Visual Analysis of Sentiment Divergence in Social Media[J]. Journal of Visualization, 2015, 18(2): 221-235.
[42] Joseph K, Wintoki M B, Zhang Z.Forecasting Abnormal Stock Returns and Trading Volume Using Investor Sentiment: Evidence from Online Search[J]. International Journal of Forecasting, 2011, 27(4): 1116-1127.
[43] Lampos V, Cristianini N.Tracking the Flu Pandemic by Monitoring the Social Web[C]// Proceedings of the 2nd International Workshop on Cognitive Information Processing. IEEE, 2010: 411-416.
[44] El-Assady M, Gold V, Acevedo C, et al.ConToVi: Multi‐Party Conversation Exploration Using Topic-Space Views[J]. Computer Graphics Forum, 2016, 35(3): 431-440.
[45] Kase S E, Roy H E, Bowman E K, et al.Visualizing Host-Nation Sentiment at the Tactical Edge[C]// Proceedings of the 19th International Command and Control Research and Technology Symposium. 2014.
[1] Zhongxi You,Weina Hua,Xuelian Pan. Matching Book Reviews and Essential Sentiment Lexicons with Chinese Word Segmenters[J]. 数据分析与知识发现, 2019, 3(7): 23-33.
[2] Cuiqing Jiang,Yibo Guo,Yao Liu. Constructing a Domain Sentiment Lexicon Based on Chinese Social Media Text[J]. 数据分析与知识发现, 2019, 3(2): 98-107.
[3] Bengong Yu,Peihang Zhang,Qingtang Xu. Selecting Products Based on F-BiGRU Sentiment Analysis[J]. 数据分析与知识发现, 2018, 2(9): 22-30.
[4] Ziming Zeng,Qianwen Yang. Sentiment Analysis for Micro-blogs with LDA and AdaBoost[J]. 数据分析与知识发现, 2018, 2(8): 51-59.
[5] Xiufang Wang,Shu Sheng,Yan Lu. Analyzing Public Opinion from Microblog with Topic Clustering and Sentiment Intensity[J]. 数据分析与知识发现, 2018, 2(6): 37-47.
[6] Tingting Wang,Kaiping Wang,Guijie Qi. Analyzing Implemented Ideas from Open Innovation Platform with Sentiment Analysis: Case Study of Salesforce[J]. 数据分析与知识发现, 2018, 2(4): 38-47.
[7] Yang Zhao,Qiqi Li,Yuhan Chen,Wenhang Cao. Examining Consumer Reviews of Overseas Shopping APP with Sentiment Analysis[J]. 数据分析与知识发现, 2018, 2(11): 19-27.
[8] Yue He,Can Zhu. Sentiment Analysis of Weibo Opinion Leaders——Case Study of “Illegal Vaccine” Event[J]. 数据分析与知识发现, 2017, 1(9): 65-73.
[9] Hongli Zhang,Jiying Liu,Sinan Yang,Jian Xu. Predicting Online Users’ Ratings with Comments[J]. 数据分析与知识发现, 2017, 1(8): 48-58.
[10] Ge Gao,Junmei Luo,Yu Wang. Analyzing Textual Sentiment Based on HNC Theory[J]. 数据分析与知识发现, 2017, 1(8): 85-91.
[11] Huanrong Shou,Shuqing Deng,Jian Xu. Detecting Online Rumors with Sentiment Analysis[J]. 数据分析与知识发现, 2017, 1(7): 44-51.
[12] Chuanming Yu,Bolin Feng,Lu An. Sentiment Analysis in Cross-Domain Environment with Deep Representative Learning[J]. 数据分析与知识发现, 2017, 1(7): 73-81.
[13] Xinhui Dun,Yunqiu Zhang,Kaixi Yang. Fine-grained Sentiment Analysis Based on Weibo[J]. 数据分析与知识发现, 2017, 1(7): 61-72.
[14] Weifang Wu,Baojun Gao,Haixia Yang,Hanlin Sun. The Impacts of Reviews on Hotel Satisfaction: A Sentiment Analysis Method[J]. 数据分析与知识发现, 2017, 1(3): 62-71.
[15] Shuang Yang,Fen Chen. Analyzing Sentiments of Micro-blog Posts Based on Support Vector Machine[J]. 数据分析与知识发现, 2017, 1(2): 73-79.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938