Detecting correlations between hot days in news feeds

RaghvenPhDa Mall, Nahil Jain, Vikram Pudi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We use text mining mechanisms to analyze Hot days in news feeds. We build upon the earlier work used to detect Hot topics and assume that we have already attained the Hot days. In this paper we identify the most relevant documents of a topic on a Hot day. We construct a similarity based technique for identifying and ranking these documents. Our aim is to automatically detect chains of hot correlated events over time. We develop a scheme using similarity measures like cosine similarity and KL-divergence to find correlation between these Hot days. For the 'U.S. Presidential Elections', the presidential debates which spanned over a week was one such event.

Original languageEnglish
Title of host publicationKDIR 2011 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval
Pages375-378
Number of pages4
Publication statusPublished - 1 Dec 2011
Externally publishedYes
EventInternational Conference on Knowledge Discovery and Information Retrieval, KDIR 2011 - Paris, France
Duration: 26 Oct 201129 Oct 2011

Other

OtherInternational Conference on Knowledge Discovery and Information Retrieval, KDIR 2011
CountryFrance
CityParis
Period26/10/1129/10/11

Keywords

  • Correlated hot events
  • Derived hotness
  • Deriving hot topics

ASJC Scopus subject areas

  • Information Systems

Cite this

Mall, R., Jain, N., & Pudi, V. (2011). Detecting correlations between hot days in news feeds. In KDIR 2011 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (pp. 375-378)