Advertisement

An Overview of Sentiment Analysis in Social Media and Its Applications in Disaster Relief

  • Ghazaleh BeigiEmail author
  • Xia Hu
  • Ross Maciejewski
  • Huan Liu
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 639)

Abstract

Sentiment analysis refers to the class of computational and natural language processing based techniques used to identify, extract or characterize subjective information, such as opinions, expressed in a given piece of text. The main purpose of sentiment analysis is to classify a writer’s attitude towards various topics into positive, negative or neutral categories. Sentiment analysis has many applications in different domains including, but not limited to, business intelligence, politics, sociology, etc. Recent years, on the other hand, have witnessed the advent of social networking websites, microblogs, wikis and Web applications and consequently, an unprecedented growth in user-generated data is poised for sentiment mining. Data such as web-postings, Tweets, videos, etc., all express opinions on various topics and events, offer immense opportunities to study and analyze human opinions and sentiment. In this chapter, we study the information published by individuals in social media in cases of natural disasters and emergencies and investigate if such information could be used by first responders to improve situational awareness and crisis management. In particular, we explore applications of sentiment analysis and demonstrate how sentiment mining in social media can be exploited to determine how local crowds react during a disaster, and how such information can be used to improve disaster management. Such information can also be used to help assess the extent of the devastation and find people who are in specific need during an emergency situation. We first provide the formal definition of sentiment analysis in social media and cover traditional and the state-of-the-art approaches while highlighting contributions, shortcomings, and pitfalls due to the composition of online media streams. Next we discuss the relationship among social media, disaster relief and situational awareness and explain how social media is used in these contexts with the focus on sentiment analysis. In order to enable quick analysis of real-time geo-distributed data, we will detail applications of visual analytics with an emphasis on sentiment visualization . Finally, we conclude the chapter with a discussion of research challenges in sentiment analysis and its application in disaster relief.

Keywords

Sentiment analysis Disaster relief Visualization Social media 

Notes

Acknowledgments

This material is based upon the work supported by, or in part by, Office of Naval Research (ONR) under grant number N000141410095, the NSF under Grant Number 1350573, and the U.S. Department of Homeland Security’s VACCINE Center under Award Number 2009-ST-061-CI0001.

References

  1. 1.
    Zafarani, R., Abbasi, M.A., Liu, H.: Social Media Mining: An Introduction. Cambridge University Press, Cambridge (2014)CrossRefGoogle Scholar
  2. 2.
    Leskovec, J., Huttenlocher, D., Kleinberg, J.: Predicting positive and negative links in online social networks. In: Proceedings of the 19th International Conference on World Wide Web, pp. 641–650. ACM (2010)Google Scholar
  3. 3.
    Beigi, G., Jalili, M., Alvari, H., Sukthankar, G.: Leveraging community detection for accurate trust prediction. In: ASE International Conference on Social Computing, June 2014Google Scholar
  4. 4.
    Tang, J., Xia, H., Liu, H.: Social recommendation: a review. Soc. Netw. Anal. Min. 3(4), 1113–1133 (2013)CrossRefGoogle Scholar
  5. 5.
    Alvari, H., Hajibagheri, A., Sukthankar, G.: Community detection in dynamic social networks: a game-theoretic approach. In: 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 101–107. IEEE (2014)Google Scholar
  6. 6.
    Goetz, M., Leskovec, J., McGlohon, M., Faloutsos, C.: Modeling blog dynamics (2009)Google Scholar
  7. 7.
    Alvari, H., Hashemi, S., Hamzeh, A.: Discovering overlapping communities in social networks: a novel game-theoretic approach. AI Commun. 26(2), 161–177 (2013)MathSciNetzbMATHGoogle Scholar
  8. 8.
    Lindsay, B.R.: Social Media and Disasters: Current Uses, Future Options, and Policy Considerations. Technical report, Congressional Research Service, SeptemberGoogle Scholar
  9. 9.
    Cobo, A., Parra, D., Navón, J.: Identifying relevant messages in a twitter-based citizen channel for natural disaster situations. In: Proceedings of the 24th International Conference on World Wide Web Companion, pp. 1189–1194 (2015)Google Scholar
  10. 10.
    Gao, H., Barbier, G., Goolsby, R.: Harnessing the crowdsourcing power of social media for disaster relief. IEEE Intell. Syst. 3, 10–14 (2011)CrossRefGoogle Scholar
  11. 11.
    Kumar, S., Morstatter, F., Liu, H.: Twitter Data Analytics. Springer, New York (2014)CrossRefGoogle Scholar
  12. 12.
    Liu, B.: Sentiment Analysis and Opinion Mining. Synth. Lect. Hum. Lang. Technol. 5(1), 1–167. Morgan & Claypool Publishers (2012)Google Scholar
  13. 13.
    Nasukawa, T., Yi, J.: Sentiment analysis: capturing favorability using natural language processing. In: Proceedings of the 2nd International Conference on Knowledge Capture, K-CAP’03, pp. 70–77. ACM, New York (2003)Google Scholar
  14. 14.
    Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2(1–2), 1–135 (2008)CrossRefGoogle Scholar
  15. 15.
    Das, S., Chen, M.: Yahoo! for amazon: extracting market sentiment from stock message boards. In: Asia Pacific Finance Association Annual Conference (APFA) (2001)Google Scholar
  16. 16.
    Morinaga, S., Yamanishi, K., Tateishi, K., Fukushima, T.: Mining product reputations on the web. In: Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pp. 341–349 (2002). Industry trackGoogle Scholar
  17. 17.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79–86. Association for Computational Linguistics (2002)Google Scholar
  18. 18.
    Tong, R.M.: An operational system for detecting and tracking opinions in on-line discussion. In: Proceedings of the Workshop on Operational Text Classification (OTC) (2001)Google Scholar
  19. 19.
    Turney, P.D.: Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews (2002). arXiv:cs.LG/0212032
  20. 20.
    Wiebe, J.: Learning subjective adjectives from corpora. In: Kautz, H.A., Porter, B.W. (eds.) AAAI/IAAI, pp. 735–740. AAAI Press/The MIT Press (2000)Google Scholar
  21. 21.
    Carlson, G.: Review of “subjective understanding, computer models of belief systems Jaime G. Carbonell”. UMI Research Press, Ann Arbor Michigan Copyright 1981. SIGART Bull. 80, 12 (1982)Google Scholar
  22. 22.
    Wilks, Y., Bien, J.: Beliefs, points of view and multiple environments. In: Proceedings of the International NATO Symposium on Artificial and Human Intelligence, pp. 147–171. Elsevier North-Holland Inc., New York (1984)Google Scholar
  23. 23.
    Hearst, M.: Direction-based text interpretation as an information access refinement. In: Jacobs, P. (ed.) Text-Based Intelligent Systems, pp. 257–274. Lawrence Erlbaum Associates (1992)Google Scholar
  24. 24.
    Huettner, A., Subasic, P.: Fuzzy typing for document management. In: ACL 2000 Companion Volume: Tutorial Abstracts and Demonstration Notes, pp. 26–27 (2000)Google Scholar
  25. 25.
    Sack, W.: On the computation of point of view. In: Proceedings of the Twelfth National Conference on Artificial Intelligence, AAAI’94. American Association for Artificial Intelligence, Menlo Park, CA, USA, vol. 2, p. 1488 (1994)Google Scholar
  26. 26.
    Wiebe, J., Bruce, R.: Probabilistic classifiers for tracking point of view. In: Working Notes of the AAAI Spring Symposium on Empirical Methods in Discourse Interpretation (1995)Google Scholar
  27. 27.
    Wiebe, J.M.: Identifying subjective characters in narrative. In: Proceedings of the 13th Conference on Computational Linguistics, COLING’90, vol. 2, pp. 401–406. Association for Computational Linguistics, Stroudsburg (1990)Google Scholar
  28. 28.
    Wiebe, J.M.: Tracking point of view in narrative. Comput. Linguist. 20(2), 233–287 (1994)Google Scholar
  29. 29.
    Wiebe, J.M., Bruce, R.F., O’Hara, T.P.: Development and use of a gold-standard data set for subjectivity classifications. In: Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics on Computational Linguistics, ACL’99, pp. 246–253. Association for Computational Linguistics, Stroudsburg (1999)Google Scholar
  30. 30.
    Wiebe, J., Rapaport, W.J.: A computational theory of perspective and reference in narrative. In: Hobbs, J.R. (ed.) ACL, pp. 131–138 (1988)Google Scholar
  31. 31.
    Kouloumpis, E., Wilson, T., Moore, J.: Twitter sentiment analysis: the good the bad and the omg!. ICWSM 11, 538–541 (2011)Google Scholar
  32. 32.
    Qiu, G., Liu, B., Bu, J., Chen, C.: Expanding domain sentiment lexicon through double propagation. In: Boutilier, C. (ed.) IJCAI, pp. 1199–1204 (2009)Google Scholar
  33. 33.
    Thelwall, M., Buckley, K., Paltoglou, G.: Sentiment in twitter events. J. Am. Soc. Inf. Sci. Technol. 62(2), 406–418 (2011)CrossRefGoogle Scholar
  34. 34.
    Tan, C., Lee, L., Tang, J., Jiang, L., Zhou, M., Li, P.: User-level sentiment analysis incorporating social networks. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1397–1405. ACM (2011)Google Scholar
  35. 35.
    Hu, X., Tang, L., Tang, J., Liu, H.: Exploiting social relations for sentiment analysis in microblogging. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, pp. 537–546. ACM (2013)Google Scholar
  36. 36.
    Zhao, J., Dong, L., Wu, J., Xu, K.: Moodlens: an emoticon-based sentiment analysis system for Chinese tweets. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1528–1531. ACM (2012)Google Scholar
  37. 37.
    Hu, X., Tang, J., Gao, H., Liu, H.: Unsupervised sentiment analysis with emotional signals. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 607–618. International World Wide Web Conferences Steering Committee (2013)Google Scholar
  38. 38.
    Tang, D., Wei, F., Yang, N., Zhou, M., Liu, T., Qin, B.: Learning sentiment-specific word embedding for twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014, June 22–27, 2014, Baltimore, MD, USA, Long Papers, vol. 1, pp. 1555–1565 (2014)Google Scholar
  39. 39.
    Saif, H., He, Y., Alani, H.: Semantic sentiment analysis of twitter. In: Cudré-Mauroux, P., et al. (eds.) The Semantic Web-ISWC 2012, pp. 508–524. Springer, Berlin (2012)CrossRefGoogle Scholar
  40. 40.
    Kucuktunc, O., Cambazoglu, B.B., Weber, I., Ferhatosmanoglu, H.: A large-scale sentiment analysis for yahoo! answers. In: Proceedings of the fifth ACM International Conference on Web Search and Data Mining, pp. 633–642. ACM (2012)Google Scholar
  41. 41.
    Wu, L., Zhou, Y., Tan, F., Yang, F., Li, J.: Generating syntactic tree templates for feature-based opinion mining. In: Tang, J., King, I., Chen, L., Wang, J. (eds.) Advanced Data Mining and Applications, pp. 1–12. Springer, Berlin (2011)CrossRefGoogle Scholar
  42. 42.
    Schapire, E.R., Singer, Y.: Boostexter: a boosting-based system for text categorization. Mach. Learn. 39(2), 135–168 (2000)CrossRefzbMATHGoogle Scholar
  43. 43.
    Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., Kappas, A.: Sentiment strength detection in short informal text. J. Am. Soc. Inf. Sci. Technol. 61(12), 2544–2558 (2010)CrossRefGoogle Scholar
  44. 44.
    Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168–177. ACM (2004)Google Scholar
  45. 45.
    Fu, B., Lin, J., Li, L., Faloutsos, C., Hong, J., Sadeh, N.: Why people hate your app: making sense of user feedback in a mobile app store. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1276–1284. ACM (2013)Google Scholar
  46. 46.
    Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. J. Comput. Sci. 2(1), 1–8 (2011)CrossRefGoogle Scholar
  47. 47.
    Hennig, P., Berger, P., Lehmann, C., Mascher, A., Meinel, C.: Accelerate the detection of trends by using sentiment analysis within the blogosphere. In: 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 503–508, Aug 2014Google Scholar
  48. 48.
    Manning, C.D., Schütze, H.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge (1999)zbMATHGoogle Scholar
  49. 49.
    Nigam, K., Lafferty, J., McCallum, A.: Using maximum entropy for text classification. In: IJCAI-99 Workshop on Machine Learning for Information Filtering, vol. 1, pp. 61–67 (1999)Google Scholar
  50. 50.
    Cristianini, N., Shawe-Taylor, J.: An introduction to support vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, Cambridge (2000)CrossRefzbMATHGoogle Scholar
  51. 51.
    Wilson, T., Hoffmann, P., Somasundaran, S., Kessler, J., Wiebe, J., Choi, Y., Cardie, C., Riloff, E., Patwardhan, S.: Opinionfinder: a system for subjectivity analysis. In: Proceedings of HLT/EMNLP on Interactive Demonstrations, pp. 34–35. Association for Computational Linguistics (2005)Google Scholar
  52. 52.
    Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S.J., McClosky, D.: The stanford corenlp natural language processing toolkit. In: Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 55–60 (2014)Google Scholar
  53. 53.
    McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: Homophily in Social Networks. Annual Review of Sociology, pp. 415–444 (2001)Google Scholar
  54. 54.
    Abelson, R.P.: Whatever became of consistency theory?. Pers. Soc. Psychol. Bull. Sage Publications (1983)Google Scholar
  55. 55.
    Hatfield, E., Cacioppo, J.T., Rapson, R.L.: Emotional Contagion. Cambridge University Press, Cambridge (1994)Google Scholar
  56. 56.
    Friedman, J., Hastie, T., Tibshirani, R.: The Elements of Statistical Learning. Springer Series in Statistics, vol. 1. Springer, Berlin (2001)zbMATHGoogle Scholar
  57. 57.
    Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224N Project report, Stanford, pp. 1–12 (2009)Google Scholar
  58. 58.
    Read, J.: Using emoticons to reduce dependency in machine learning techniques for sentiment classification. In: Proceedings of the ACL Student Research Workshop, pp. 43–48. Association for Computational Linguistics (2005)Google Scholar
  59. 59.
    Ding, C., Li, T., Peng, W., Park, H.: Orthogonal nonnegative matrix t-factorizations for clustering. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 126–135. ACM (2006)Google Scholar
  60. 60.
    Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 1189–1232 (2001)Google Scholar
  61. 61.
    Ye, J., Chow, J.-H., Chen, J., Zheng, Z.: Stochastic gradient boosted distributed decision trees. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, pp. 2061–2064. ACM (2009)Google Scholar
  62. 62.
    Hu, Y., Wang, F., Kambhampati, S.: Listening to the crowd: automated analysis of events via aggregated twitter sentiment. In: Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, pp. 2640–2646. AAAI Press (2013)Google Scholar
  63. 63.
    Yuheng, H., John, A., Wang, F., Kambhampati, S.: Et-lda: joint topic modeling for aligning events and their twitter feedback. AAAI 12, 59–65 (2012)Google Scholar
  64. 64.
    Balahur, A., Steinberger, R., Kabadjov, M., Zavarella, V., Van Der Goot, E., Halkia, M., Pouliquen, B., Belyaeva, J.: Sentiment analysis in the news (2013). arXiv:1309.6202
  65. 65.
    Chae, J., Thom, D., Bosch, H., Jang, Y., Maciejewski, R., Ebert, D.S., Ertl, T.: Spatiotemporal social media analytics for abnormal event detection and examination using seasonal-trend decomposition. In: 2012 IEEE Conference on Visual Analytics Science and Technology (VAST), pp. 143–152. IEEE (2012)Google Scholar
  66. 66.
    Zhao, J., Cao, N., Wen, Z., Song, Y., Lin, Y.-R., Collins, C.M.: # fluxflow: visual analysis of anomalous information spreading on social media. IEEE Trans.Vis. Comput. Graph. 20(12), 1773–1782 (2014)Google Scholar
  67. 67.
    Yafeng, L., Wang, F., Maciejewski, R.: Business intelligence from social media: a study from the vast box office challenge. IEEE Comput. Graph. Appl. 34(5), 58–69 (2014)CrossRefGoogle Scholar
  68. 68.
    Gregory, M.L., Payne, D., McColgin, D., Cramer, N., Love, D.: Visual analysis of weblog content. In: ICWSM (2007)Google Scholar
  69. 69.
    Gregory, M.L., Chinchor, N., Whitney, P., Carter, R., Hetzler, E., Turner, A.: User-directed sentiment analysis: visualizing the affective content of documents. In: Proceedings of the Workshop on Sentiment and Subjectivity in Text, pp. 23–30. Association for Computational Linguistics (2006)Google Scholar
  70. 70.
    Gamon, M., Aue, A., Corston-Oliver, S., Ringger, E.: Pulse: mining customer opinions from free text. In: Famili, A.F., Kok, J.N., Peña, J.M., Siebes, A., Feelders, A. (eds.) Advances in Intelligent Data Analysis VI, pp. 121–132. Springer, Berlin (2005)CrossRefGoogle Scholar
  71. 71.
    Smith, M.A., Fiore, A.T.: Visualization components for persistent conversations. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 136–143. ACM (2001)Google Scholar
  72. 72.
    Nigam, K., McCallum, A.K., Thrun, S., Mitchell, T.: Text classification from labeled and unlabeled documents using em. Mach. Learn. 39(2–3), 103–134 (2000)CrossRefzbMATHGoogle Scholar
  73. 73.
    Oelke, D., Hao, M., Rohrdantz, C., Keim, D., Dayal, U., Haug, L.-E., Janetzko, H. et al.: Visual opinion analysis of customer feedback data. In: IEEE Symposium on Visual Analytics Science and Technology, 2009. VAST 2009, pp. 187–194. IEEE (2009)Google Scholar
  74. 74.
    Duan, D., Qian, W., Pan, S., Shi, L., Lin, C.: Visa: a visual sentiment analysis system. In: Proceedings of the 5th International Symposium on Visual Information Communication and Interaction, pp. 22–28. ACM (2012)Google Scholar
  75. 75.
    Wei, F., Liu, S., Song, Y., Pan, S., Zhou, M.X., Qian, W., Shi, L., Tan, L., Zhang, Q.: Tiara: a visual exploratory text analytic system. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 153–162. ACM (2010)Google Scholar
  76. 76.
    Adams, B., Phung, D., Venkatesh, S.: Eventscapes: visualizing events over time with emotive facets. In: Proceedings of the 19th ACM International Conference on Multimedia, pp. 1477–1480. ACM (2011)Google Scholar
  77. 77.
    Marcus, A., Bernstein, M.S., Badar, O., Karger, D.R., Madden, S., Miller, R.C.: Twitinfo: aggregating and visualizing microblogs for event exploration. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 227–236. ACM (2011)Google Scholar
  78. 78.
    Hubmann-Haidvogel, A., Brasoveanu, A.M.P., Scharl, A., Sabou, M., Gindl, S.: Visualizing contextual and dynamic features of micropost streams (2012)Google Scholar
  79. 79.
    Shook, E., Leetaru, K., Cao, G., Padmanabhan, A., Wang, S.: Happy or not: generating topic-based emotional heatmaps for culturomics using cybergis. In: 2012 IEEE 8th International Conference on E-Science (e-Science), pp. 1–6, Oct 2012Google Scholar
  80. 80.
    Guha-Sapir, D., Vos, F., Below, R., Ponserre, S.: Annual disaster statistical review 2010. Centre for Research on the Epidemiology of Disasters (2011)Google Scholar
  81. 81.
    Mejova, Y., Weber, I., Macy, M.W.: Twitter: A Digital Socioscope. Cambridge University Press, Cambridge (2015)CrossRefGoogle Scholar
  82. 82.
    Glaser, M.: California wildfire coverage by local media, blogs, twitter, maps and more. PBS MediaShift (2007)Google Scholar
  83. 83.
    Starbird, K., Palen, L., Hughes, A.L., Vieweg, S.: Chatter on the red: what hazards threat reveals about the social life of microblogged information. In: Proceedings of the 2010 ACM Conference on Computer Supported Cooperative Work, pp. 241–250. ACM (2010)Google Scholar
  84. 84.
    Sutton, J., Palen, L., Shklovski, I.: Backchannels on the front lines: emergent uses of social media in the 2007 southern california wildfires. In: Proceedings of the 5th International ISCRAM Conference, Washington, DC, pp. 624–632 (2008)Google Scholar
  85. 85.
    Vieweg, S., Hughes, A.L., Starbird, K., Palen, L.: Microblogging during two natural hazards events: what twitter may contribute to situational awareness. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1079–1088. ACM (2010)Google Scholar
  86. 86.
    Qu, Y., Wu, P.F., Wang, X.: Online community response to major disaster: a study of tianya forum in the 2008 sichuan earthquake. In: 42nd Hawaii International Conference on System Sciences, 2009. HICSS’09, pp. 1–11. IEEE (2009)Google Scholar
  87. 87.
    Qu, Y., Huang, C., Zhang, P., Zhang, J.: Microblogging after a major disaster in china: a case study of the 2010 yushu earthquake. In: Proceedings of the ACM 2011 Conference on Computer Supported Cooperative Work, pp. 25–34. ACM (2011)Google Scholar
  88. 88.
    Sakaki, T., Toriumi, F., Uchiyama, K., Matsuo, Y., Shinoda, K., Kazama, K., Kurihara, S., Noda, I.: The possibility of social media analysis for disaster management. In: Humanitarian Technology Conference (R10-HTC), 2013 IEEE Region 10, pp. 238–243. IEEE (2013)Google Scholar
  89. 89.
    Verma, S., Vieweg, S., Corvey, W.J., Palen, L., Martin, J.H., Palmer, M., Schram, A., Anderson, K.M.: Natural language processing to the rescue? Extracting “situational awareness” tweets during mass emergency. In: ICWSM (2011)Google Scholar
  90. 90.
    Terpstra, T., de Vries, A., Stronkman, R., Paradies, G.L.: Towards a realtime twitter analysis during crises for operational crisis management. In: Proceedings of the 9th International ISCRAM Conference, Vancouver, Canada (2012)Google Scholar
  91. 91.
    Morstatter, F., Lubold, N., Pon-Barry, H., Pfeffer, J., Liu, H.: Finding eyewitness tweets during crises (2014). arXiv:1403.1773
  92. 92.
    Imran, M., Elbassuoni, S., Castillo, C., Diaz, F., Meier, P.: Practical extraction of disaster-relevant information from social media. In: Proceedings of the 22nd International Conference on World Wide Web Companion, pp. 1021–1024. International World Wide Web Conferences Steering Committee (2013)Google Scholar
  93. 93.
    Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes twitter users: real-time event detection by social sensors. In: Proceedings of the 19th International Conference on World Wide Web, pp. 851–860. ACM (2010)Google Scholar
  94. 94.
    Li, H., Guevara, N., Herndon, N., Caragea, D., Neppalli, K., Caragea, C., Squicciarini, A., Tapia, A.H.: Twitter mining for disaster response: a domain adaptation approachGoogle Scholar
  95. 95.
    Imran, M., Elbassuoni, S.M., Castillo, C., Diaz, F., Meier, P.: Extracting information nuggets from disaster-related messages in social media. In: Proceedings of ISCRAM, Baden-Baden, Germany (2013)Google Scholar
  96. 96.
    Chowdhury, S.R., Imran, M., Asghar, M.R., Amer-Yahia, S., Castillo, C.: Tweet4act: using incident-specific profiles for classifying crisis-related messages. In: 10th International ISCRAM Conference (2013)Google Scholar
  97. 97.
    Truong, B., Caragea, C., Squicciarini, A., Tapia, A.H.: Identifying valuable information from twitter during natural disasters. Proc. Am. Soc. Inf. Sci. Technol. 51(1), 1–4 (2014)Google Scholar
  98. 98.
    Ashktorab, Z., Brown, C., Nandi, M., Culotta, A.: Tweedr: Mining twitter to inform disaster response. In: Proceedings of ISCRAM (2014)Google Scholar
  99. 99.
    Sen, A., Rudrat, K., Ghosh, S.: Extracting situational awareness from microblogs during disaster eventsGoogle Scholar
  100. 100.
    Mendoza, M., Poblete, B., Castillo, C.: Twitter under crisis: can we trust what we rt? In: Proceedings of the First Workshop on Social Media Analytics, pp. 71–79. ACM (2010)Google Scholar
  101. 101.
    Athanasia, N., Stavros, P.T.: Twitter as an instrument for crisis response: the typhoon haiyan case studyGoogle Scholar
  102. 102.
    MacEachren, A.M., Robinson, A.C., Jaiswal, A., Pezanowski, S., Savelyev, A., Blanford, J., Mitra, P.: Geo-twitter analytics: applications in crisis management. In: 25th International Cartographic Conference, pp. 3–8 (2011)Google Scholar
  103. 103.
    Kumar, S., Barbier, G., Abbasi, M.A., Liu, H.: Tweettracker: an analysis tool for humanitarian and disaster relief. In: ICWSM (2011)Google Scholar
  104. 104.
    Morstatter, F., Kumar, S., Liu, H., Maciejewski, R.: Understanding twitter data with tweetxplorer. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1482–1485. ACM (2013)Google Scholar
  105. 105.
    Calderon, N., Arias-Hernandez, R., Fisher, B., et al.: Studying animation for real-time visual analytics: a design study of social media analytics in emergency management. In: 2014 47th Hawaii International Conference on System Sciences (HICSS), pp. 1364–1373. IEEE (2014)Google Scholar
  106. 106.
    MacEachren, A.M., Jaiswal, A., Robinson, A.C., Pezanowski, S., Savelyev, A., Mitra, P., Zhang, X., Blanford, J.: Senseplace2: geotwitter analytics support for situational awareness. In: 2011 IEEE Conference on Visual Analytics Science and Technology (VAST), pp. 181–190. IEEE (2011)Google Scholar
  107. 107.
    Ren, D., Zhang, X., Wang, Z., Li, J., Yuan, X.: Weiboevents: a crowd sourcing weibo visual analytic system. In: Pacific Visualization Symposium (PacificVis), 2014 IEEE, pp. 330–334. IEEE (2014)Google Scholar
  108. 108.
    Abel, F., Hauff, C., Houben, G.-J., Stronkman, R., Tao, K.: Semantics + filtering + search = twitcident. exploring information in social web streams. In: Proceedings of the 23rd ACM Conference on Hypertext and Social Media, HT’12, pp. 285–294. ACM, New York (2012)Google Scholar
  109. 109.
    Nagy, A., Stamberger, J.: Crowd sentiment detection during disasters and crises. In Proceedings of the 9th International ISCRAM Conference, pp. 1–9 (2012)Google Scholar
  110. 110.
    Mandel, B., Culotta, A., Boulahanis, J., Stark, D., Lewis, B., Rodrigue, J.: A demographic analysis of online sentiment during hurricane irene. In: Proceedings of the Second Workshop on Language in Social Media, pp. 27–36. Association for Computational Linguistics (2012)Google Scholar
  111. 111.
    Dong, H., Halem, M., Zhou, S.: Social media data analytics applied to hurricane sandy. In: 2013 International Conference on Social Computing (SocialCom), pp. 963–966. IEEE (2013)Google Scholar
  112. 112.
    Schulz, A., Thanh, T., Paulheim, H., Schweizer, I.: A fine-grained sentiment analysis approach for detecting crisis related microposts. In: ISCRAM 2013 (2013)Google Scholar
  113. 113.
    Brynielsson, J., Johansson, F., Westling, A.: Learning to classify emotional content in crisis-related tweets. In: 2013 IEEE International Conference on Intelligence and Security Informatics (ISI), pp. 33–38. IEEE (2013)Google Scholar
  114. 114.
    Caragea, C., Squicciarini, A., Stehle, S., Neppalli, K., Tapia, A.: Mapping moods: geo-mapped sentiment analysis during hurricane sandy. In: Proceedings of ISCRAM (2014)Google Scholar
  115. 115.
    Kryvasheyeu, Y., Chen, H., Moro, E., Van Hentenryck, P., Cebrian, M.: Performance of social network sensors during hurricane sandy. PLoS one 10, e0117288–e0117288 (2015)CrossRefGoogle Scholar
  116. 116.
    Simon, T., Goldberg, A., Aharonson-Daniel, L., Leykin, D., Adini, B.: Twitter in the cross firethe use of social media in the westgate mall terror attack in Kenya (2014)Google Scholar
  117. 117.
    Vo, B.K.H., Collier, N.: Twitter emotion analysis in earthquake situations. Int. J. Comput. Linguist. Appl. 4(1), 159–173 (2013)Google Scholar
  118. 118.
    Torkildson, M.K., Starbird, K., Aragon, C.: Analysis and visualization of sentiment and emotion on crisis tweets. In: Luo, Y. (ed.) Cooperative Design, Visualization, and Engineering, pp. 64–67. Springer, New York (2014)Google Scholar
  119. 119.
    Buscaldi, D., Hernandez-Farias, I.: Sentiment analysis on microblogs for natural disasters management: a study on the 2014 genoa floodings. In: Proceedings of the 24th International Conference on World Wide Web Companion. International World Wide Web Conferences Steering Committee, pp. 1185–1188 (2015)Google Scholar
  120. 120.
    Lu, Y., Hu, X., Wang, F., Kumar, S., Liu, H., Maciejewski, R.: Visualizing social media sentiment in disaster scenarios. In: Proceedings of the 24th International Conference on World Wide Web Companion. International World Wide Web Conferences Steering Committee, pp. 1211–1215 (2015)Google Scholar
  121. 121.
    Varga, I., Sano, M., Torisawa, K., Hashimoto, C., Ohtake, K., Kawai, T., Jong-Hoon, O., De Saeger, S.: Aid is out there: Looking for help from tweets during a large scale disaster. In: ACL, vol. 1, pp. 1619–1629 (2013)Google Scholar
  122. 122.
    Chakraborty, B., Banerjee, S.: Modeling the evolution of post disaster social awareness from social web sites. In: 2013 IEEE International Conference on Cybernetics (CYBCONF), pp. 51–56. IEEE (2013)Google Scholar
  123. 123.
    Riloff, E., Wiebe, J.: Learning extraction patterns for subjective expressions. In: Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, pp. 105–112. Association for Computational Linguistics (2003)Google Scholar
  124. 124.
    Wiebe, J., Riloff, E.: Creating subjective and objective sentence classifiers from unannotated texts. In: Gelbukh, A. (ed.) Computational Linguistics and Intelligent Text Processing, pp. 486–497. Springer, Berlin (2005)Google Scholar
  125. 125.
    Esuli, A., Sebastiani, F.: Sentiwordnet: a publicly available lexical resource for opinion mining. In: Proceedings of LREC, vol. 6, pp. 417–422 (2006)Google Scholar
  126. 126.
    Nielsen, F.Å.: A new anew: evaluation of a word list for sentiment analysis in microblogs (2011). arXiv:1103.2903
  127. 127.
    Granger, C.W.J.: Investigating causal relations by econometric models and cross-spectral methods. Econometrica: J. Econometric Soc. 424–438 (1969)Google Scholar
  128. 128.
    Brooks, M., Kuksenok, K., Torkildson, M.K., Perry, D., Robinson, J.J., Scott, T.J., Anicello, O., Zukowski, A., Harris, P., Aragon, C.R.: Statistical affect detection in collaborative chat. In: Proceedings of the 2013 Conference on Computer Supported Cooperative Work, pp. 317–328. ACM (2013)Google Scholar
  129. 129.
    Hernandez-Farias, I., Buscaldi, D., Priego-Sánchez, B.: Iradabe: adapting english lexicons to the Italian sentiment polarity classification task. In: First Italian Conference on Computational Linguistics (CLiC-it 2014) and the Fourth International Workshop EVALITA2014, pp. 75–81 (2014)Google Scholar
  130. 130.
    Baccianella, S., Esuli, A., Sebastiani, F.: Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: LREC, vol. 10, pp. 2200–2204 (2010)Google Scholar
  131. 131.
    Socher, R., Perelygin, A., Wu, J.Y., Chuang, J., Manning, C.D., Ng, A.Y., Potts, C.: Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), vol. 1631, pp. 1642 (2013)Google Scholar
  132. 132.
    Thelwall, M., Buckley, K., Paltoglou, G.: Sentiment strength detection for the social web. J. Am. Soc. Inf. Sci. Technol. 63(1), 163–173 (2012)CrossRefGoogle Scholar
  133. 133.
    Dagan, I., Engelson, S.P.: Committee-based sampling for training probabilistic classifiers. In: Proceedings of the Twelfth International Conference on Machine Learning, pp. 150–157 (1995)Google Scholar
  134. 134.
    Topsy Labs: www.topsylabs.com (2012). Accessed 20 Nov 2012
  135. 135.
    Pennebaker, J.W., Francis, M.E., Booth, R.J.: Linguistic inquiry and word count: Liwc 2001. Mahway: Lawrence Erlbaum Associates, p. 71 (2001)Google Scholar
  136. 136.
    Best, D.M., Bruce, J.R., Dowson, S.T., Love, O.J., McGrath, L.R.: Web-based visual analytics for social media. Technical report, Pacific Northwest National Laboratory (PNNL), Richland, WA (US) (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Ghazaleh Beigi
    • 1
    Email author
  • Xia Hu
    • 2
  • Ross Maciejewski
    • 1
  • Huan Liu
    • 1
  1. 1.Computer Science and EngineeringArizona State UniversityTempeUSA
  2. 2.Department of Computer Science and EngineeringTexas A&M UniversityCollege StationUSA

Personalised recommendations