Ipoll: Automatic polling using online search
dc.contributor.author | Nguyen, T. | |
dc.contributor.author | Phung, D. | |
dc.contributor.author | Luo, W. | |
dc.contributor.author | Tran, The Truyen | |
dc.contributor.author | Venkatesh, S. | |
dc.date.accessioned | 2017-01-30T15:17:54Z | |
dc.date.available | 2017-01-30T15:17:54Z | |
dc.date.created | 2015-10-29T04:09:59Z | |
dc.date.issued | 2014 | |
dc.identifier.citation | Nguyen, T. and Phung, D. and Luo, W. and Tran, T.T. and Venkatesh, S. 2014. Ipoll: Automatic polling using online search. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 8786: pp. 266-275. | |
dc.identifier.uri | http://hdl.handle.net/20.500.11937/45036 | |
dc.description.abstract |
© Springer International Publishing Switzerland 2014. For years, opinion polls rely on data collected through telephone or person-to-person surveys. The process is costly, inconvenient, and slow. Recently online search data has emerged as potential proxies for the survey data. However considerable human involvement is still needed for the selection of search indices, a task that requires knowledge of both the target issue and how search terms are used by the online community. The robustness of such manually selected search indices can be questionable. In this paper, we propose an automatic polling system through a novel application of machine learning. In this system, the needs for examining, comparing, and selecting search indices have been eliminated through automatic generation of candidate search indices and intelligent combination of the indices. The results include a publicly accessible web application that provides real-time, robust, and accurate measurements of public opinions on several subjects of general interest. | |
dc.publisher | Springer Verlag | |
dc.title | Ipoll: Automatic polling using online search | |
dc.type | Journal Article | |
dcterms.source.volume | 8786 | |
dcterms.source.startPage | 266 | |
dcterms.source.endPage | 275 | |
dcterms.source.issn | 0302-9743 | |
dcterms.source.title | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | |
curtin.department | Multi-Sensor Proc & Content Analysis Institute | |
curtin.accessStatus | Fulltext not available |
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