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Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (2): 11-18    DOI: 10.11925/infotech.2096-3467.2017.02.02
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An Early Warning Algorithm for Public Opinion of Safety Emergency
Shihai Tian,Deli Lyu()
School of Management, Harbin University of Science and Technology, Harbin 150040, China
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[Objective] This study proposes a new early warning model to track the public sentiment online, aiming to improve transparency and responding speed of the safety emergencies. [Methods] We used the modified LSA+SVM algorithm to build an early warning model, which retrieved public opinion data by meta search. [Results] We examined the new model with three different incidents, and found it was practical and fast. The precision rate was 85.75% when the semantic dimension was kept at 10. [Limitations] This method was more effective for the safety incidents drawing public attention and discussion. [Conclusions] The proposed algorithm helps us build an early warning system for public opinion, which provides suggestions to related companies and government organizations.

Key wordsLatent Semantic Analysis(LSA)      Support Vector Machine(SVM)      Public Opinion Early Warning      Emotional Orientation Analysis     
Received: 29 August 2016      Published: 27 March 2017

Cite this article:

Shihai Tian,Deli Lyu. An Early Warning Algorithm for Public Opinion of Safety Emergency. Data Analysis and Knowledge Discovery, 2017, 1(2): 11-18.

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