Leveraging cross-media analytics to detect events and mine opinions for emergency management

Purpose Timely detection of emergency events and effective tracking of corresponding public opinions are critical in emergency management. As media are immediate sources of information on emergencies, this paper proposes cross-media analytics to detect and track emergency events and provides decision support for government and emergency management departments. Design/methodology/approach In this paper, a novel emergency event detection and opinion mining method is proposed for emergency management using cross-media analytics. In the proposed approach, an event detection module is constructed to discover emergency events based on cross-media analytics, and after the detected event is confirmed as an emergency event, an opinion mining module is used to analyze public sentiments and then generate public sentiment time series for early-warning via a semantic expansion technique. Findings Empirical results indicate that a specific emergency can be detected and that public opinion can be tracked effectively and...

[1]  Hae-Chang Rim,et al.  Lexicalized Hidden Markov Models for Part-of-Speech Tagging , 2000, COLING.

[2]  P. Kolesar,et al.  Improving Emergency Responsiveness with Management Science , 2004 .

[3]  Chi-Lun Liu,et al.  Ontological subscription and blocking system that alleviates information overload in social blogs , 2014, Knowl. Based Syst..

[4]  Bing Liu,et al.  Opinion Mining , 2009, Encyclopedia of Database Systems.

[5]  Andrea Esuli,et al.  SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining , 2006, LREC.

[6]  Peter J. Kolesar,et al.  ANNIVERSARY ARTICLE: Improving Emergency Responsiveness with Management Science , 2004, Manag. Sci..

[7]  Stephan M. Winkler,et al.  On Text Preprocessing for Opinion Mining Outside of Laboratory Environments , 2012, AMT.

[8]  Hinrich Schütze,et al.  Introduction to information retrieval , 2008 .

[9]  Joel Peress,et al.  Media Coverage and the Cross-Section of Stock Returns , 2008 .

[10]  Rada Mihalcea,et al.  TextRank: Bringing Order into Text , 2004, EMNLP.

[11]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[12]  Karin M. Verspoor,et al.  Biomedical Text Mining: State-of-the-Art, Open Problems and Future Challenges , 2014, Interactive Knowledge Discovery and Data Mining in Biomedical Informatics.

[13]  Michal Karpowicz,et al.  Opinion Mining on the Web 2.0 - Characteristics of User Generated Content and Their Impacts , 2013, CHI-KDD.

[14]  Heng-Li Yang,et al.  Applying ontology-based blog to detect information system post-development change requests conflicts , 2012, Inf. Syst. Frontiers.

[15]  Chenchuramaiah T. Bathala.  Giving Content to Investor Sentiment: The Role of Media in the Stock Market , 2007 .

[16]  Mingliang Chen,et al.  Building emotional dictionary for sentiment analysis of online news , 2014, World Wide Web.

[17]  Juan Carlos SanMiguel,et al.  An Ontology for Event Detection and its Application in Surveillance Video , 2009, 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance.

[18]  Peter D. Turney.  Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL , 2001, ECML.

[19]  Álvaro García-Martín,et al.  An Ontology for Event Detection and its Application in Surveillance Video , 2009, AVSS.

[20]  Daniela Fogli,et al.  Knowledge-centered design of decision support systems for emergency management , 2013, Decis. Support Syst..

[21]  Desheng Dash Wu,et al.  Using text mining and sentiment analysis for online forums hotspot detection and forecast , 2010, Decis. Support Syst..

[22]  Din J. Wasem,et al.  Mining of Massive Datasets , 2014 .

[23]  Sean Hughes,et al.  Clustering by Fast Search and Find of Density Peaks , 2016 .

[24]  Tim Loughran,et al.  When is a Liability not a Liability? Textual Analysis, Dictionaries, and 10-Ks , 2010 .

[25]  H. Raghav Rao,et al.  Community Intelligence and Social Media Services: A Rumor Theoretic Analysis of Tweets During Social Crises , 2013, MIS Q..

[26]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[27]  Anand Rajaraman,et al.  Mining of Massive Datasets , 2011 .

[28]  Kenneth Ward Church.  A Stochastic Parts Program and Noun Phrase Parser for Unrestricted Text , 1988, ANLP.

[29]  Ana M. García-Serrano,et al.  Q-WordNet: Extracting Polarity from WordNet Senses , 2010, LREC.

[30]  Thomas Gottron,et al.  Bad news travel fast: a content-based analysis of interestingness on Twitter , 2011, WebSci '11.

[31]  Michal Karpowicz,et al.  Computational approaches for mining user's opinions on the Web 2.0 , 2014, Inf. Process. Manag..

[32]  W. Waugh,et al.  Collaboration and Leadership for Effective Emergency Management , 2006 .