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

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Sentiment Analysis and Ontology Engineering

Part of the book series: Studies in Computational Intelligence ((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.

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Notes

  1. 1.

    http://Weibo.com.

  2. 2.

    http://answers.yahoo.com.

  3. 3.

    http://trendsmap.com/.

  4. 4.

    http://www.twitalyzer.com/.

  5. 5.

    http://ouseful.open.ac.uk/geotwitterous/.

  6. 6.

    http://www.opencalais.com.

  7. 7.

    http://noslang.com.

  8. 8.

    http://www.alchemyapi.com/.

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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.

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Authors and Affiliations

  1. Computer Science and Engineering, Arizona State University, Tempe, USA

    Ghazaleh Beigi, Ross Maciejewski & Huan Liu

  2. Department of Computer Science and Engineering, Texas A&M University, College Station, USA

    Xia Hu

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  1. Ghazaleh Beigi
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  2. Xia Hu
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  3. Ross Maciejewski
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  4. Huan Liu
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Correspondence to Ghazaleh Beigi .

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  1. Department of Electrical and Computer, University of Alberta, Edmonton, Alberta, Canada

    Witold Pedrycz

  2. and Technology, National Taiwan University of Scien, Taipei, Taiwan

    Shyi-Ming Chen

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Beigi, G., Hu, X., Maciejewski, R., Liu, H. (2016). An Overview of Sentiment Analysis in Social Media and Its Applications in Disaster Relief. In: Pedrycz, W., Chen, SM. (eds) Sentiment Analysis and Ontology Engineering. Studies in Computational Intelligence, vol 639. Springer, Cham. https://doi.org/10.1007/978-3-319-30319-2_13

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