Sentiment analysis during Hurricane Sandy in emergency response

Abstract Sentiment analysis has been widely researched in the domain of online review sites with the aim of generating summarized opinions of users about different aspects of products. However, there has been little work focusing on identifying the polarity of sentiments expressed by users during disaster events. Identifying such sentiments from online social networking sites can help emergency responders understand the dynamics of the network, e.g., the main users' concerns, panics, and the emotional impacts of interactions among members. In this paper, we perform a sentiment analysis of tweets posted on Twitter during the disastrous Hurricane Sandy and visualize online users' sentiments on a geographical map centered around the hurricane. We show how users' sentiments change according not only to their locations, but also based on the distance from the disaster. In addition, we study how the divergence of sentiments in a tweet posted during the hurricane affects the tweet retweetability. We find that extracting sentiments during a disaster may help emergency responders develop stronger situational awareness of the disaster zone itself.

[1]  Andrea Esuli,et al.  SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining , 2010, LREC.

[2]  Cornelia Caragea,et al.  Mapping moods: Geo-mapped sentiment analysis during hurricane sandy , 2014, ISCRAM.

[3]  Cornelia Caragea,et al.  Retweetability Analysis and Prediction during Hurricane Sandy , 2016, ISCRAM.

[4]  Frank Schweitzer,et al.  Emotional Divergence Influences Information Spreading in Twitter , 2012, ICWSM.

[5]  Andrea H. Tapia,et al.  Beyond the trustworthy tweet: A deeper understanding of microblogged data use by disaster response and humanitarian relief organizations , 2013, ISCRAM.

[6]  Amanda Lee Hughes,et al.  Online Media as a Means to Affect Public Trust in Emergency Responders , 2015, ISCRAM.

[7]  Robert Thomson,et al.  Trusting tweets: The Fukushima disaster and information source credibility on Twitter , 2012, ISCRAM.

[8]  Barbara Poblete,et al.  Twitter under crisis: can we trust what we RT? , 2010, SOMA '10.

[9]  Leysia Palen,et al.  The Evolving Role of the Public Information Officer: An Examination of Social Media in Emergency Management , 2012 .

[10]  Leysia Palen,et al.  Chatter on the red: what hazards threat reveals about the social life of microblogged information , 2010, CSCW '10.

[11]  Leysia Palen,et al.  Mastering social media: An analysis of Jefferson County's communications during the 2013 Colorado floods , 2014, ISCRAM.

[12]  Kristina Lerman,et al.  Information Contagion: An Empirical Study of the Spread of News on Digg and Twitter Social Networks , 2010, ICWSM.

[13]  Finn Årup,et al.  A new ANEW: Evaluation of a word list for sentiment analysis in microblogs , 2016 .

[14]  Bo Pang,et al.  Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.

[15]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[16]  Axel Schulz,et al.  A fine-grained sentiment analysis approach for detecting crisis related microposts , 2013, ISCRAM.

[17]  Mike Wells,et al.  Structured Models for Fine-to-Coarse Sentiment Analysis , 2007, ACL.

[18]  Bing Liu,et al.  Mining and summarizing customer reviews , 2004, KDD.

[19]  Satoshi Kurihara,et al.  Regional analysis of user interactions on social media in times of disaster , 2013, WWW '13 Companion.

[20]  Rizal Setya Perdana What is Twitter , 2013 .

[21]  Xiao Zhang,et al.  SensePlace2: GeoTwitter analytics support for situational awareness , 2011, 2011 IEEE Conference on Visual Analytics Science and Technology (VAST).

[22]  Finn Årup Nielsen,et al.  A New ANEW: Evaluation of a Word List for Sentiment Analysis in Microblogs , 2011, #MSM.

[23]  Anthony C. Robinson,et al.  Leveraging geospatially-oriented social media communications in disaster response , 2012, ISCRAM.

[24]  Sean Fitzhugh,et al.  Connected communications: Network structures of official communications in a technological disaster , 2012, ISCRAM.

[25]  Jeannie A. Stamberger,et al.  Crowd sentiment detection during disasters and crises , 2012, ISCRAM.

[26]  Kees Nieuwenhuis,et al.  Information Systems for Crisis Response and Management , 2007, Mobile Response.

[27]  Hosung Park,et al.  What is Twitter, a social network or a news media? , 2010, WWW '10.

[28]  Leysia Palen,et al.  Microblogging during two natural hazards events: what twitter may contribute to situational awareness , 2010, CHI.

[29]  A. Culotta,et al.  A Demographic Analysis of Online Sentiment during Hurricane Irene , 2012 .

[30]  Yutaka Matsuo,et al.  Earthquake shakes Twitter users: real-time event detection by social sensors , 2010, WWW '10.

[31]  Aron Culotta,et al.  Tweedr: Mining twitter to inform disaster response , 2014, ISCRAM.

[32]  Sophia B. Liu,et al.  The New Cartographers: Crisis Map Mashups and the Emergence of Neogeographic Practice , 2010 .

[33]  Leysia Palen,et al.  Online public communications by police & fire services during the 2012 Hurricane Sandy , 2014, CHI.

[34]  Malik Magdon-Ismail,et al.  Information Cascades in Social Media in Response to a Crisis : a Preliminary Model and a Case Study , 2012 .

[35]  Cornelia Caragea,et al.  Twitter Mining for Disaster Response: A Domain Adaptation Approach , 2015, ISCRAM.

[36]  John Yen,et al.  Classifying text messages for the haiti earthquake , 2011, ISCRAM.

[37]  John Yen,et al.  Seeking the trustworthy tweet: Can microblogged data fit the information needs of disaster response and humanitarian relief organizations , 2011, ISCRAM.

[38]  Sean Fitzhugh,et al.  Terse message amplification in the Boston bombing response , 2014, ISCRAM.

[39]  Leysia Palen,et al.  Twitter adoption and use in mass convergence and emergency events , 2009 .

[40]  Petra Saskia Bayerl,et al.  Social media and the police: tweeting practices of british police forces during the August 2011 riots , 2013, CHI.

[41]  Cornelia Caragea,et al.  Identifying Informative Messages in Disasters using Convolutional Neural Networks , 2016, ISCRAM.

[42]  Barbara Poblete,et al.  Information credibility on twitter , 2011, WWW.

[43]  Shady Elbassuoni,et al.  Practical extraction of disaster-relevant information from social media , 2013, WWW.