基于時間演化圖卷積網絡的輿情熱點內容預測

文雅 楊頻 廖珊 代金鞘 賈鵬

有效預測輿情事件的熱點內容有利于提高對輿論導向的把控能力和對公眾訴求的預判能力. 然而，現有的輿情預測工作大多關注事件整體趨勢指標或情感極性的演變預測，鮮有針對輿情事件熱點內容的預測研究. 為解決以上問題，本文提出一種基于時間演化圖卷積網絡的輿情熱點內容預測方法：以輿情事件的熱點詞作為預測對象，首先，通過演化圖卷積網絡學習各時間片詞語的空間關聯關系；然后，使用門控循環單元捕捉各時間片詞語特征的時序變化；最后，通過全連接層進行輸出，實現對輿情事件熱點詞的預測. 以微博上兩個不同的輿情突發事件的相關文本作為數據集，與兩種現有熱點詞預測方法開展對比實驗. 實驗結果表明，該方法在兩個數據集上的精確率分別達到51.21%和50.98%，召回率分別達到50.17%和48.15%，F1值分別達到50.68%和49.52%，均高于兩種對比方法，能夠更好地完成輿情事件中熱點詞的預測.

輿情預測； 熱點詞預測； 時間演化圖卷積網絡

TP391.1A2023.033001

收稿日期： 2022-11-01

基金項目： 四川省科技廳重點研發項目（2021YFG0156）

作者簡介： 文雅（1997-）， 女， 碩士研究生， 主要研究領域為輿情分析與預測. E-mail： tanya_scu@163.com

通訊作者： 楊頻. E-mail： yangpin@scu.edu.cn

A temporal evolving graph convolutional network for Public opinion prediction in emergencies

WEN Ya1， YANG Pin1， LIAO Shan2， DAI Jin-Qiao1， JIA Peng1

（1. College of Cybersecurity， Sichuan University， Chengdu 610211， China；

2. The 30th Research Institute of China Electronics Technology Group Corporation， Chengdu 610041， China）

Public opinion prediction is one of the key solutions to improve the ability to guide public opinion in emergencies. However， most of the existing public opinion prediction work focuses on the trend indicator or sentiment polarity of events ，while little attention paid to the prediction of hot words and topics in specific events. In this paper， a temporal evolving graph convolutional network for public opinion prediction in emergencies is proposed， in which the hot words associated with specific events are taken as the object of public opinion prediction. Our approach combines evolving graph convolutional network with gated recurrent unit： the former is used to learn the dynamic spatial correlation between words and the latter is used to capture the temporal changes of words， the hot words of an emergency in the next time period is then predicted through full connection layer output. To validate the proposed method， we selected discussion texts related to two emergencies on Weibo as the dataset， and conducted comparative experiments with two existing hot word prediction methods. The results show that our method achieved higher precision， recall， and F1-score in both emergencies， with precision of 51.21% and 50.98%， recall of 50.17% and 48.15%， and F1-scores of 50.68% and 49.52%， respectively. These results demonstrate that our proposed method is effective in predicting public opinion during emergencies

Public opinion prediction; Hot words prediction; Temporal evolving graph convolutional network

1 引 言

輿情指輿論情況，是指在輿情因變事項（下文簡稱輿情事件）發生、發展和轉變過程中，民眾所持有的看法、觀點和態度等［1］. 隨著互聯網和自媒體的發展，以前只會從事件發源地慢慢擴散流傳的輿情事件，現在則很快通過網絡散播并……