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10.1109/ICDM.2015.112 http://scihub22266oqcxt.onion/10.1109/ICDM.2015.112 C4811610!4811610!27034625     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=27034625&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Proc+IEEE+Int+Conf+Data+Min 2015 ; 2015 (ä): 429-38 Nephropedia Template TP {{tp|p=27034625|t=2015. Mining Multi-Aspect Reflection of News Events in Twitter: Discovery, Linking and Presentation |pdf=|usr=}}{{27034625}} gab.com Text Mining Multi-Aspect Reflection of News Events in Twitter: Discovery, Linking and Presentation JO: Proc IEEE Int Conf Data Min http://www.kidney.de/pget.php?&tw=1&pmid=27034625 #FOAvip A major event often has repercussions on both news media and microblogging sites such as Twitter. Reports from mainstream news agencies and discussions from Twitter complement each other to form a complete picture. An event can have multiple aspects (sub-events) describing it from multiple angles, each of which attracts opinions/comments posted on Twitter. Mining such reflections is interesting to both policy makers and ordinary people seeking information. In this paper, we propose a unified framework to mine multi-aspect reflections of news events in Twitter. We propose a novel and efficient dynamic hierarchical entity-aware event discovery model to learn news events and their multiple aspects. The aspects of an event are linked to their reflections in Twitter by a bootstrapped dataless classification scheme, which elegantly handles the challenges of selecting informative tweets under overwhelming noise and bridging the vocabularies of news and tweets. In addition, we demonstrate that our framework naturally generates an informative presentation of each event with entity graphs, time spans, news summaries and tweet highlights to facilitate user digestion. Twit Text FOAVip Mining Multi-Aspect Reflection of News Events in Twitter: Discovery, Linking and Presentation ????Proc IEEE Int Conf Data Min http://www.kidney.de/pget.php?&tw=1&pmid=27034625 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27034625 #FOAvip English Wikipedia <ref>{{Cite pmid|27034625}}</ref> Mining Multi-Aspect Reflection of News Events in Twitter: Discovery, Linking and Presentation #MMPMID27034625 Wang J; Tong W; Yu H; Li M; Ma X; Cai H; Hanratty T; Han J Proc IEEE Int Conf Data Min 2015[Nov]; 2015 (ä): 429-38 PMID27034625show ga A major event often has repercussions on both news media and microblogging sites such as Twitter. Reports from mainstream news agencies and discussions from Twitter complement each other to form a complete picture. An event can have multiple aspects (sub-events) describing it from multiple angles, each of which attracts opinions/comments posted on Twitter. Mining such reflections is interesting to both policy makers and ordinary people seeking information. In this paper, we propose a unified framework to mine multi-aspect reflections of news events in Twitter. We propose a novel and efficient dynamic hierarchical entity-aware event discovery model to learn news events and their multiple aspects. The aspects of an event are linked to their reflections in Twitter by a bootstrapped dataless classification scheme, which elegantly handles the challenges of selecting informative tweets under overwhelming noise and bridging the vocabularies of news and tweets. In addition, we demonstrate that our framework naturally generates an informative presentation of each event with entity graphs, time spans, news summaries and tweet highlights to facilitate user digestion. äMining Multi-Aspect Reflection of News Events in Twitter: Discovery, L(epmc).pdf DeepDyve Pubget Overpricing