【目的/意義】為了實現突發事件網絡輿情熱點話題的及時發現與捕捉,實現多角度、全方位、高精度的網絡輿情突發事件監測,精準構建特定時間區間內網絡輿情突發事件的知識圖譜監測模型對于輿情內容的監測和突發話題的發現具有重要影響。【方法/過程】本文基于知識圖譜理論,提出了一種新的網絡輿情監測方法,以突發事件網絡輿情的時間特征為切入點,通過突發詞項識別、構建突發話題圖以及語義補充與完善三個步驟,在保留突發事件特征的基礎上有效過濾無關網絡內容,構建包含語義關系的突發話題圖,實現全方面、高精度、少噪音的突發事件網絡輿情熱點話題監測。最后,本文以全標注微博數據集與在線微博數據流為基礎展開實驗研究。【結果/結論】實驗結果表明:基于知識圖譜的網絡輿情監測方法有效提升了突發事件網絡輿情監測的準確性與全面性,相較于傳統的網絡輿情監測算法,其突發事件監測準確率與召回率提升幅度大于6%,F1得分提升幅度大于12%,即通過篩選突發詞項、構建突發話題圖、語義補充與完善三個步驟,基于知識圖譜的網絡輿情監測方法在理論層面上有效提升了突發事件網絡輿情監測的準確性與全面性,對于及時發現網絡輿情話題、精確捕捉網絡輿情發展趨勢、針對性防治網絡輿情危機等具有重要的指導意義。

【Purpose/significance】To detect emerging topics in online public opinion as well as monitor user-generated contents on social media platforms, the online public opinion monitoring and early warning function module realizes the detection of social emergencies based on the knowledge map theory.【Method/process】This paper propose a novel approachbased on knowledge graph. The general idea is combining diverse features of terms and their multi-correlations in a topicgraph and the method relies on a 3-steps process: Emerging Terms Identification, Topic Graph Construction and SemanticEnrichment. Furthermore, a novel mechanism of term selection and semantic enrichment for graph-based topic detection isproposed to facilitate eliminating noises and extracting more comprehensive information from data streams. Extensive exper-iments on the dataset verify the effectiveness of our algorithm over several benchmarks.【Result/conclusion】Both topic preci-sion and recall have been increased by at least 6% with our approaches, reflecting the accuracy of topic detection in real-life events’ emerging time period.F1 score for term extraction has been increased by at least12%. It means the quality ofterms extracted for describing an emerging topic has been ameliorated.