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An Early Warning Algorithm for Public Opinion of Safety Emergency |
Tian Shihai, Lyu Deli() |
School of Management, Harbin University of Science and Technology, Harbin 150040, China |
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Abstract [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.
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Received: 29 August 2016
Published: 27 March 2017
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