摘要：

为辅助投资者在短期内及时发现投资热点,结合财经新闻的特点,提出一种财经新闻话题检测模型。构建基于财经新闻的时间窗切分新闻流,根据新闻文本中的主题事件、特征词、新闻语义及金融命名实体提取文本特征,并应用最近邻-凝聚层次聚类算法获得话题簇。实验结果表明,与传统多特征话题检测模型相比,该模型可有效降低聚类算法运行时间,提高话题检测准确度,且在一定程度上协助投资者进行决策判断。

关键词: 财经新闻, 话题检测, 多特征融合, 凝聚层次聚类, K最近邻

Abstract:

In order to help investors find hot spots of investment in a short time,this paper combines the characteristics of the financial news and proposes a financial news topic detection model.The model constructs a time window based on financial news to segment news streams,combines the topic events,feature words,news semantics and financial name entities to extract text features,and applies the Nearest Neighbor-Hierarchical Agglomerative Clustering(NNHAC) algorithm to get the topic clusters.Experimental results show that,compared with tranditional multi-feature topic detection models,this model can effectively reduce the running time of the clustering algorithm,improve the accuracy of topic detection,and to a certain extent,it helps investors to make decision and judgement.

Key words: financial news, topic detection, multi-feature fusion, Hierarchical Agglomerative Clustering(HAC), K-Nearest Neighbor(KNN)

中图分类号: 

• TP391.1