|
|
- 2016
基于事件的新闻客户端热门评论预测框架
|
Abstract:
摘要： 将评论作为主要研究对象,提出了一种基于事件的新闻客户端热门评论预测框架。为了解决单个新闻客户端数据稀疏的问题,利用新闻客户端的聚集性来挖掘事件;通过建立事件背景解决了使用单条新闻进行预测带来的冷启动问题;框架内部各模块关系完全松耦合,能够依据不同的事件粒度进行在线的热门评论的预测。最后通过实例实验证明,使用框架中提出的联合客户端数据的事件挖掘策略,能够很好地避免单个客户端中数据稀疏的问题,同时证明基于事件进行热门评论框架的效果要优于单纯使用评论本身。
Abstract: A framework based on event detection is proposed to do popular comments prediction in news Apps. Taking advantage of the aggregation of news Apps, the problem of sparse data for a single news App is avoided. Also, in this framework, events are detected as the context of comments to solve the cold-start problem; components are loosely coupled, which means it can adapt all kinds of granularity of events. We provide an instance of this framework and it turns out that using the event detection strategy mentioned above, the sparse data problem no longer exists. Whats more, the framework brings a better prediction result than using the comment itself
| [1] | ALLAN J, CARBONELL J G, DODDINGTON G, et al. Topic detection and tracking pilot study final report[J]. Proceedings of DARPA Broadcast News Transcription and Understanding Workshop. San Franciscu: Morgan Kaufmann Publishers, 1998: 194-218. |
| [2] | LEE D D, SEUNG H S. Learning the parts of objects by non-negative matrix factorization[J]. Nature, 1999, 401(6755):788-791. |
| [3] | HE Q, CHANG K, LIM E P, et al. Keep it simple with time: a reexamination of probabilistic topic detection models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(10):1795-1808. |
| [4] | LANDAUER T K, FOLTZ P W, LAHAM D. An introduction to latent semantic analysis[J]. Discourse Processes, 1998, 25(2-3):259-284. |
| [5] | BLEI D M, NG A Y, JORDAN M I. Latent dirichletallocation[J]. The Journal of Machine Learning Research, 2003, 3:993-1022. |
| [6] | BLEI D M, LAFFERTY J D. Dynamic topic models[C] // Proceedings of the 23rd International Conference on Machine Learning. New York: ACM Press, 2006: 113-120. |
| [7] | 戴臻.布谷鸟算法优化支持向量机的网络热点话题预测[J].计算机应用与软件,2014,(4):330-333. DAI Zhen. Internet hot topic prediction based on support vector machine optimised by cuckoo search algorithm[J]. Computer Applications and Software, 2014,(4):330-333. |
| [8] | 李玉梅.基于互联网评论的股票市场趋势预测[D].哈尔滨:哈尔滨工业大学,2012. |
| [9] | 李雪妮.基于评论情感和自回归模型的销量预测研究[D].大连:大连理工大学,2013. |
| [10] | 聂卉.基于内容分析的用户评论质量的评价与预测[J].图书情报工作,2014(13):83-89. NIE Hui. Content-oriented evaluation and detection for product reviews[J]. Library and Information Service, 2014(13):83-89. |
| [11] | 张跃伟.基于微博客话题的热点预测及传播溯源[D]. 北京:北京邮电大学, 2014. |
| [12] | FISCUS J G, DODDINGTON G R. Topic detection and tracking evaluation overview[M]. New York: Springer, 2002: 17-31. |
| [13] | TONG H, LIU Y, PENG H, et al. Internet users' psychosocial attention prediction: Web hot topic prediction based on adaptive AR model[C] // Proceedings of the International Conference on Computer Science and Information Technology. Washington: IEEE Computer Society, 2008: 458-462. |
| [14] | WANG Y, AGICHTEIN E, BENZI M. Tm-lda: efficient online modeling of latent topic transitions in social media[C] // Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 2012: 123-131. |
| [15] | VACA C K, MANTRACH A, JAIMES A, et al. A time-based collective factorization for topic discovery and monitoring in news[C] // Proceedings of the 23rd International Conference on World Wide Web. New York: ACM Press, 2014: 527-538. |
| [16] | 徐涛.基于神经网络的BBS热点话题预测方法研究[D].杭州:杭州电子科技大学,2010. |
