Stream-based live public opinion monitoring approach with adaptive probabilistic topic model
Stream-based live public opinion monitoring approach with adaptive probabilistic topic model
复制标题
基于流的自适应概率主题模型实时舆情监测方法
DOI：
10.1007/s00500-018-3391-7
复制
发表时间：
2019
期刊：
影响因子：
4.1
通讯作者：
Yang Bo
中科院分区：
文献类型：
--
作者：
Ma Kun;Yu Ziqiang;Ji Ke;Yang Bo
舆情监测，又称第一故事检测，是指对特定网络新闻事件的话题检测和跟踪。一般来说，它是用来寻找新闻传播。传统的方法采用文本匹配来解决意见监测问题。但它也存在一些局限性，如对大规模数据的隐藏和潜在主题的发现以及匹配结果的相关性排序不正确等。本文提出了三种实时舆情监测的解决方案：简单关键词计算与匹配、简单概率话题计算与匹配和基于流的实时概率话题计算与匹配。指出了前两种解决方案的不足之处，如语义匹配和实时大数据效率低。提出了基于流的实时主题计算和主题匹配，查询时文档和字段提升，以作出实质性的改进。最后，通过对抓取的网易历史新闻记录进行主题计算和匹配实验，验证了本文方法的有效性。
Public opinion monitoring, also known as first story detection, is defined within the topic detection and tracking on a particular Internet news event. Generally, it is used to find news propagation. Traditional method adopts text matching to address opinion monitoring. But it has some limitations such as hidden and latent topic discovery and incorrect relevance ranking of matching results on large-scale data. In this paper, we propose three solutions to live public opinion monitoring: simple keyword computing and matching, simple probabilistic topic computing and matching, and stream-based live probabilistic topic computing and matching. We point out the disadvantages of the first two solutions such as semantic matching and low efficiency on timely big data. Stream-based real-time topic computing and topic matching with query-time document and field boosting are proposed to make substantial improvements. Finally, our topic computing and matching experiments with crawled historical Netease news records show that our approaches are effective and efficient.