1 School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094, China 2 Jiangsu Collaborative Innovation Center of Social Safety Science and Technology, Nanjing 210094, China, Nanjing 210094, China
[Objective] This study combines the external features and contents of the Weibo posts, aiming to identify online opinion leaders with the help of text sentiment analysis. [Methods] First, we identified the potential opinion leaders and introduced the Word2Vec algorithm to find new sentiment words. Then, we conducted sentiment analysis to categorize the texts as positive, negative or neutral ones. Finally, we detected and removed bloggers attracted too many negative comments. [Results] The proposed model optimized the ranking of opinion leaders, which was better than the improved PageRank algorithm, and more consistent with the Weibo data. [Limitations] We only examined our model with one piece of breaking news. [Conclusions] This paper identifies three types of online opinion leaders from the public reaction in emergency.
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