网络评论情感可视化技术方法及工具研究
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摘要
【目的】总结分析当前网络评论情感可视化的主要技术方法及其工具,探讨其发展的主要趋势。【方法】在对近年相关文献进行调研的基础上,根据网络评论情感分析可视化技术方法的特点,进行归纳总结;从交互性和定制特征,对可视化工具进行分类和应用特点分析。【结果】将网络评论情感可视化技术方法归纳为:基于文本内容的情感可视化、基于时空的情感可视化和基于文本主题的情感可视化。将可视化工具总结为静态、交互式以及支持编程三种类型。【结论】本文对网络评论情感可视化技术方法及其工具进行归纳、总结和分类,阐述了网络评论情感可视化发展的三个主要趋势,以期为情感可视化和相关研究及可视化工具的选择提供参考。
[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.
[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.
引文
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[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.(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.(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.(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.(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.)
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[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.(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].https://en.wikipedia.org/wiki/Bag-of-words_model.
[10]Cui W,Liu S,Tan L,et al.Text Flow: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].http://in-spire.pnnl.gov/.
[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 the2011 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.Senti Compass: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 2014IEEE 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.(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.)
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[44]El-Assady M,Gold V,Acevedo C,et al.Con To Vi:Multi‐Party Conversation Exploration Using Topic-Space Views[J].Computer Graphics Forum,2016,35(3):431-440.
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