Title :
Minority opinions abstraction
Author :
Tan Xian ; Li Fang
Author_Institution :
Hubei Inst. for Nat., Enshi, China
Abstract :
To deal with the multi-granularity web documents, we propose EMTF (EM Topic Fusion) algorithm to rank the by-joint possibility, resulting of their relevance to be the topic and their granularity. We first extract features from the different granularity web documents to establish the quantitative relationship amongst them. Then, the process of multi-granularity web documents analysis that leads to heretofore unknown information and opinions that valuable potential, minority and contentious respectively, which integrates the time, content, reprint and link information. Experiments show that EMTF achieves the best overall performance with high effectiveness and robustness.
Keywords :
Internet; abstracting; document handling; feature extraction; EM topic fusion algorithm; EMTF; by-joint possibility; feature extraction; minority opinions abstraction; multigranularity Web documents analysis; quantitative relationship; Artificial neural networks; Computational modeling; Electronic learning; Feature extraction; Internet; Ontologies; Semantics; Topic Fusion; minority and contentious; multi-granularity; potential; unknown information and opinions;
Conference_Titel :
Information Technology and Artificial Intelligence Conference (ITAIC), 2011 6th IEEE Joint International
Conference_Location :
Chongqing
Print_ISBN :
978-1-4244-8622-9
DOI :
10.1109/ITAIC.2011.6030239