Paper Info
Title
Scalable discovery of contradictions on the web
Abstract
Our study addresses the problem of large-scale contradiction detection and management, from data extracted from the Web. We describe the first systematic solution to the problem, based on a novel statistical measure for contradictions, which exploits first- and second-order moments of sentiments. Our approach enables the interactive analysis and online identification of contradictions under multiple levels of time granularity. The proposed algorithm can be used to analyze and track opinion evolution over time and to identify interesting trends and patterns. It uses an incrementally updatable data structure to achieve computational efficiency and scalability. Experiments with real datasets show promising time performance and accuracy.
Year
DOI
Venue
2010
10.1145/1772690.1772871
WWW
Keywords
Field
DocType
incrementally updatable data structure,promising time performance,large-scale contradiction detection,novel statistical measure,computational efficiency,interesting trend,interactive analysis,multiple level,scalable discovery,online identification,time granularity,data structure,opinion mining,second order
Data mining,Interactive analysis,Computer science,Artificial intelligence,Granularity,Contradiction,Data structure,Online identification,World Wide Web,Sentiment analysis,Exploit,Machine learning,Scalability
Conference
Citations 
PageRank 
References 
19
1.34
7
Authors
3
Name
Order
Citations
PageRank
Mikalai Tsytsarau11759.13
Themis Palpanas2113691.61
Kerstin Denecke314023.57