[Purpose/significance] Microblog is an important medium of communicating public opinions toward public emergencies. Excavating topics and sentiment of microblogs on public emergencies has important practical significance to grasp the online public opinions, and to identify and predict the potential problems and risks during public emergencies. In this study, we propose an approach to analyze the topical and emotional evolution of microblog public opinions on public emergencies. [Method/process] The Zika outbreak is taken as an example and the life cycle of related microblog public opinions is divided into several phases. Topics are extracted from microblogs by the word2vec technique. The sentiment analysis has been conducted based on dictionaries containing sentiment words and emoticons to categorize the sentiment of microblogs at a fine-grained level. The emotional intensity of microblogs for each topic is also calculated to achieve synergetic analysis of topics and sentiment of microblogs. [Result/conclusion] The proposed method can reveal the topical and emotional features and emotional intensity of microblogs on specific public emergencies and illustrate the synergetic evolution patterns of topics and sentiment of online public opinions.
An Lu
,
Wu Lin
. An Integrated Analysis of Topical and Emotional Evolution of Microblog Public Opinions on Public Emergencies[J]. Library and Information Service, 2017
, 61(15)
: 120
-129
.
DOI: 10.13266/j.issn.0252-3116.2017.15.014
[1] 纪雪梅. 特定事件情境下中文微博用户情感挖掘与传播研究[D]. 天津:南开大学, 2014.
[2] 新浪新闻中心.关于寨卡病毒,你想知道的都在这里[EB/OL].[2017-07-02]. http://news.sina.com.cn/o/2016-02-04/doc-ifxpfhzk8888256.shtml.
[3] BLEI D M, LAFFERTY J D. Dynamic topic models[C]//Proceedings of the 23rd international conference on machine learning. New York:ACM, 2006:113-120.
[4] GRIFFITHS T L, STEYVERS M. Finding scientific topics[J]. Proceedings of the national academy of sciences, 2004, 101(s1):5228-5235.
[5] ALSUMAIT L, BARBARÁ D, DOMENICONI C. On-line LDA:adaptive topic models for mining text streams with applications to topic detection and tracking[C]//Proceedings of the 8th IEEE international conference on data mining (ICDM'08). Italy:IEEE, 2008:3-12.
[6] MIKOLOV T, SUTSKEVER I, CHEN K, et al. Distributed representations of words and phrases and their compositionality[C]//Proceedings of the 26th international conference on neural information processing systems (NIPS'13). Nevada:Neural Information Processing Systems, 2013:3111-3119.
[7] 朱雪梅. 基于Word2Vec主题提取的微博推荐[D].北京:北京理工大学,2014.
[8] 李跃鹏, 金翠, 及俊川. 基于word2vec的关键词提取算法[J]. 科研信息化技术与应用, 2015, 6(4):54-59.
[9] 陈昀,毕海岩.基于多特征融合的中文评论情感分类算法[J]. 河北大学学报(自然科学版), 2015, 35(6):651-656.
[10] 何炎祥, 孙松涛, 牛菲菲, 等. 用于微博情感分析的一种情感语义增强的深度学习模型[J]. 计算机学报, 2017, 40(4):773-790.
[11] 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.
[12] 徐琳宏,林鸿飞,潘宇,等.情感词汇本体的构造[J]. 情报学报, 2008, 27(2):180-185.
[13] 《知网》情感分析用词语集[EB/OL].[2017-02-10]. http://www.keenage.com/html/c_bulletin_2007.htm.
[14] "National" Taiwan University Semantic Dictionary[EB/OL].[2017-02-10]. http://nlg18.csie.ntu.edu.tw:8080/opinion/publ.html.
[15] PANG B, LEE L, VAITHYANATHAN S. Thumbs up? sentiment classification using machine learning techniques[C]//Proceedings of the ACL-02 conference on empirical methods in natural language processing. Stroudsburg:Association for Computational Linguistics,2002:79-86.
[16] 刘龙飞, 杨亮, 张绍武, 等. 基于卷积神经网络的微博情感倾向性分析[J]. 中文信息学报, 2015, 29(6):159-165.
[17] 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.
[18] BURKHOLDER B T, TOOLE M J. Evolution of complex disasters[J]. The lancet, 1995, 346(8981):1012-1015.
[19] ROBERT H. Emergency management[M]. Beijing:China Citric Press, 2004:22.
[20] FINK S. Crisis management:planning for the inevitable[M]. New York:American Management Association, 1986.
[21] 马建华, 陈安. 突发事件的演化模式分析[J].安全, 2009, 30(12):1-4.
[22] 贾亚敏, 安璐, 李纲. 城市突发事件网络信息传播时序变化规律研究[J].情报杂志, 2015, 34(4):91-96.
[23] 安璐,杜廷尧,余传明,等.突发公共卫生事件的微博主题演化模式和时序趋势——以Twitter和Weibo的埃博拉微博为例[J].情报资料工作,2016(5):44-52.
[24] 蒋静.公共卫生安全类事件的网络舆情研究[D].长沙:湖南大学,2014.
[25] 朱琛. 基于概率主题模型的社会化情感分析[D]. 合肥:中国科学技术大学, 2015.
[26] EHEK R, SOJKA P. Gensim——Statistical semantics in Python[C]//Proceedings of the 4th European meeting on Python in science (EuroScipy 2011).Paris:EuroScipy, 2011:1-2.
[27] 李凌霄, 李绍滋, 曹冬林. 基于多情绪源关联模型的中文微博情感分析[J]. 智能系统学报,2016,11(4):546-553.
[28] 马秉楠, 黄永峰, 邓北星. 基于表情符的社交网络情绪词典构造[J]. 计算机工程与设计, 2016, 37(5):1129-1133.
[29] 韩忠明, 张玉沙, 张慧, 等. 有效的中文微博短文本倾向性分类算法[J]. 计算机应用与软件, 2012, 29(10):89-93.
[30] 杜振雷. 面向微博短文本的情感分析研究[D]. 北京:北京信息科技大学, 2013.
[31] 搜狗输入法词库[EB/OL].[2017-02-10]. http://pinyin.sogou.com/dict/.