Analyzing the Sentiment of Crowd for Improving the Emergency Response Services

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smartcomp18_neha.pdf (1.1 MB)

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http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8421325&isnumber=8421307

Permanent Link

10.1109/SMARTCOMP.2018.00067
 http://hdl.handle.net/11603/11219

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UMBC Information Systems Department
UMBC Faculty Collection

Author/Creator

Author/Creator ORCID

Date

2018-07-31

Type of Work

conference papers and proceedings preprints
Text

Department

Program

Citation of Original Publication

N. Singh, N. Roy and A. Gangopadhyay, "Analyzing the Sentiment of Crowd for Improving the Emergency Response Services," 2018 IEEE International Conference on Smart Computing (SMARTCOMP), Taormina, 2018, pp. 1-8.

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This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please contact the author.

Subjects

Sentiment analysis
Twitter
Cleaning
Event detection,
Detection algorithms
Data visualization
Data analysis
Mobile Pervasive & Sensor Computing Lab

Abstract

Twitter is an extremely popular micro-blogging social platform with millions of users, generating thousands of tweets per second. The huge amount of Twitter data inspire the researchers to explore the trending topics, event detection and event tracking which help to postulate the fine-grained details and situation awareness. Obtaining situational awareness of any event is crucial in various application domains such as natural calamities, man made disaster and emergency responses. In this paper, we advocate that data analytics on Twitter feeds can help improve the planning and rescue operations and services as provided by the emergency personnel in the event of unusual circumstances. We take a different approach and focus on the users' emotions, concerns and feelings expressed in tweets during the emergency situations, and analyze those feelings and perceptions in the community involved during the events to provide appropriate feedback to emergency responders and local authorities. We employ sentiment analysis and change point detection techniques to process, discover and infer the spatiotemporal sentiments of the users. We analyze the tweets from recent Las Vegas shooting (Oct. 2017) and note that the changes in the polarity of the sentiments and articulation of the emotional expressions, if captured successfully can be employed as an informative tool for providing feedback to EMS.