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.

Original languageEnglish (US)
Title of host publicationStudies in Computational Intelligence
PublisherSpringer Verlag
Pages313-340
Number of pages28
Volume639
DOIs
StatePublished - 2016

Publication series

NameStudies in Computational Intelligence
Volume639
ISSN (Print)1860949X

Fingerprint

Disasters
Competitive intelligence
World Wide Web
Websites
Visualization
Processing
Chemical analysis

Keywords

  • Disaster relief
  • Sentiment analysis
  • Social media
  • Visualization

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Beigi, G., Hu, X., Maciejewski, R., & Liu, H. (2016). An overview of sentiment analysis in social media and its applications in disaster relief. In Studies in Computational Intelligence (Vol. 639, pp. 313-340). (Studies in Computational Intelligence; Vol. 639). Springer Verlag. https://doi.org/10.1007/978-3-319-30319-2_13

An overview of sentiment analysis in social media and its applications in disaster relief. / Beigi, Ghazaleh; Hu, Xia; Maciejewski, Ross; Liu, Huan.

Studies in Computational Intelligence. Vol. 639 Springer Verlag, 2016. p. 313-340 (Studies in Computational Intelligence; Vol. 639).

Research output: Chapter in Book/Report/Conference proceedingChapter

Beigi, G, Hu, X, Maciejewski, R & Liu, H 2016, An overview of sentiment analysis in social media and its applications in disaster relief. in Studies in Computational Intelligence. vol. 639, Studies in Computational Intelligence, vol. 639, Springer Verlag, pp. 313-340. https://doi.org/10.1007/978-3-319-30319-2_13
Beigi G, Hu X, Maciejewski R, Liu H. An overview of sentiment analysis in social media and its applications in disaster relief. In Studies in Computational Intelligence. Vol. 639. Springer Verlag. 2016. p. 313-340. (Studies in Computational Intelligence). https://doi.org/10.1007/978-3-319-30319-2_13
Beigi, Ghazaleh ; Hu, Xia ; Maciejewski, Ross ; Liu, Huan. / An overview of sentiment analysis in social media and its applications in disaster relief. Studies in Computational Intelligence. Vol. 639 Springer Verlag, 2016. pp. 313-340 (Studies in Computational Intelligence).
@inbook{65ce09d447a340c7bea5d4492ac3ffe0,
title = "An overview of sentiment analysis in social media and its applications in disaster relief",
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 = "Disaster relief, Sentiment analysis, Social media, Visualization",
author = "Ghazaleh Beigi and Xia Hu and Ross Maciejewski and Huan Liu",
year = "2016",
doi = "10.1007/978-3-319-30319-2_13",
language = "English (US)",
volume = "639",
series = "Studies in Computational Intelligence",
publisher = "Springer Verlag",
pages = "313--340",
booktitle = "Studies in Computational Intelligence",

}

TY - CHAP

T1 - An overview of sentiment analysis in social media and its applications in disaster relief

AU - Beigi, Ghazaleh

AU - Hu, Xia

AU - Maciejewski, Ross

AU - Liu, Huan

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

KW - Disaster relief

KW - Sentiment analysis

KW - Social media

KW - Visualization

UR - http://www.scopus.com/inward/record.url?scp=84961644445&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84961644445&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-30319-2_13

DO - 10.1007/978-3-319-30319-2_13

M3 - Chapter

VL - 639

T3 - Studies in Computational Intelligence

SP - 313

EP - 340

BT - Studies in Computational Intelligence

PB - Springer Verlag

ER -