【目的】融合外部特征與帖文本身的內容,引入文本傾向性分析表征網民對博主的支持度,識別網絡意見領袖。【方法】構建意見領袖識別模型,在潛在意見領袖提取的基礎上,引入文本傾向性分析,通過Word2Vec算法識別網絡中的情感新詞、提高微博評論情感傾向性分析的準確性,分別計算博主評論中正面、中立和負面三種情感傾向所占的比例,去除負面情感占比過重的"偽意見領袖"。【結果】與改進的PageRank算法對比,本文意見領袖識別模型進一步優化了意見領袖的排序結果,與原始微博數據更為一致。【局限】研究語料來源于"官員毆打護士"話題,具有一定的領域局限性。【結論】模型最終識別出三種典型的網絡意見領袖,涵蓋突發事件發展的全過程。

[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.