【目的】研究面向開放網絡社區話題交互數據的對抗性觀點挖掘方法。【方法】構建基于情感分析和主題模型的觀點情感對抗性挖掘模型。通過該模型,考慮知乎社區、話題、交互數據等特征,加入交互數據篩選和關鍵詞篩選,以知乎AlphaGo話題為例進行實證研究。【結果】本文方法可有效挖掘觀點及其情感對抗性。研究發現在AlphaGo話題討論中,"挺AlphaGo"和"反AlphaGo"的對抗性顯著。"挺AlphaGo"的主要表現有人類智慧、比賽、能力,"反AlphaGo"的主要表現有AI產品及其產品、理解能力。【局限】僅針對AlphaGo主題進行實證分析,在模型泛化性驗證上有待提高。【結論】本文方法具有可操作性和可解釋性,可挖掘交互數據潛在的對抗性信息,從而使觀點挖掘的結果更具針對性,為情報分析、觀點挖掘提供借鑒。

[Objective] This paper explores data mining techniques for confrontational opinions from interaction data of online community. [Methods] First, we constructed a new algorithm to analyze emotional confrontations based on sentiment analysis and topic model. Then, we included the characteristics of knowledge, topic, and interaction data to the new model. Finally, we conducted an empirical study on the topic of AlphaGo. [Results]There was significant"Pro-AlphaGo"and"Anti-AlphaGo"confrontations online. The"Pro-AlphaGo"topics included human intelligence, competition and ability. The"Anti-AlphaGo"opinions covered AI companies,products and comprehension abilities. [Limitations] We only examined the proposed model with the topic of AlphaGo. [Conclusions] The proposed method benefits intelligence analysis.