【目的/意義】提出融合深層演化特征的情感分析方法,以提升公共安全事件微博情感分析精度。【方法/過程】以紅黃藍幼兒園涉嫌虐童事件為例,使用LDA與爬蟲軟件提取演化特征中的主題特征、時間特征,結合傳統淺層文本詞性特征與情感特征,應用于XGBoost以生成微博情感分析集成模型。【結果/結論】演化特征的融入使得情感識別準確度Auc值提高4%,且XGBoost分類精度均優于SVM、隨機森林。本文提出的情感識別模型能夠在公共安全事件微博情感分析方面取得較好效果。

【Purpose/significance】The paper proposes a sentiment analysis method incorporating deep evolutionary features to improve the accuracy of Micro-blog sentiment analysis for public safety events.【Method/process】Taking the child abuse incident in Red, yellow and blue kindergarten as an example, the theme features and time features from Micro-blog are extracted by LDA and Crawler software, combining with the traditional text POS and emotion features. At last, the features are applied to XGBoost to generate a Micro-blog sentiment analysis integrated model.【Result/conclusion】The results show that the accuracy of emotion recognition is increased by 4% with the integration of evolutionary features, and the accuracy indicators of XGBoost classification is better than that of SVM and Random Forest. This proposed model can achieve better results in the sentiment analysis of public security events.