網絡事件中,意見領袖能夠傳播信息、擴大影響力、引導輿論方向。為了識別意見領袖,文章引入多級文本傾向性分析,自動將文本識別為非常正面、正面、中立、負面和非常負面5種情感傾向,更加精確地表征網民對博主的支持度。在此基礎上,進一步融合多種用戶特征,構建系統的網絡意見領袖識別指標體系,基于新浪微博的真實數據,進行意見領袖識別,并與未加支持度指標的模型以及支持度兩級分類模型進行對比,驗證了方法的有效性。

Opinion leaders can disseminate information,expand influence and guide public opinion in network events. To identify opinion leaders,this paper introduces multilevel text sentiment analysis to classify texts into "very positive", "positive","neutral","negative",and"very negative",which can detect the support degree of netizens for bloggers more precisely. On this basis,the paper further fuses multiple user features to construct a systematic framework of identifying online opinion leaders,and uses the data from Sina Weibo for experimental study. The effectiveness and reliability of the proposed method is proved by being compared with the models without the index of support degree or without two level support degrees.