Online text classification for real life tweet analysis

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dc.citation.epage1612en_US
dc.citation.spage1609en_US
dc.contributor.authorYar, Ersinen_US
dc.contributor.authorDelibalta, İ.en_US
dc.contributor.authorBaruh, L.en_US
dc.contributor.authorKozat, Süleyman Serdaren_US
dc.coverage.spatialZonguldak, Turkeyen_US
dc.date.accessioned2018-04-12T11:48:24Z
dc.date.available2018-04-12T11:48:24Z
dc.date.issued2016en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionDate of Conference: 16-19 May 2016en_US
dc.descriptionConference Name: IEEE 24th Signal Processing and Communications Applications Conference, SIU 2016en_US
dc.description.abstractIn this paper, we study multi-class classification of tweets, where we introduce highly efficient dimensionality reduction techniques suitable for online processing of high dimensional feature vectors generated from freely-worded text. As for the real life case study, we work on tweets in the Turkish language, however, our methods are generic and can be used for other languages as clearly explained in the paper. Since we work on a real life application and the tweets are freely worded, we introduce text correction, normalization and root finding algorithms. Although text processing and classification are highly important due to many applications such as emotion recognition, advertisement selection, etc., online classification and regression algorithms over text are limited due to need for high dimensional vectors to represent natural text inputs. We overcome such limitations by showing that randomized projections and piecewise linear models can be efficiently leveraged to significantly reduce the computational cost for feature vector extraction from the tweets. Hence, we can perform multi-class tweet classification and regression in real time. We demonstrate our results over tweets collected from a real life case study where the tweets are freely-worded, e.g., with emoticons, shortened words, special characters, etc., and are unstructured. We implement several well-known machine learning algorithms as well as novel regression methods and demonstrate that we can significantly reduce the computational complexity with insignificant change in the classification and regression performance.en_US
dc.description.provenanceMade available in DSpace on 2018-04-12T11:48:24Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 179475 bytes, checksum: ea0bedeb05ac9ccfb983c327e155f0c2 (MD5) Previous issue date: 2016en
dc.identifier.doi10.1109/SIU.2016.7496063en_US
dc.identifier.urihttp://hdl.handle.net/11693/37699
dc.language.isoTurkishen_US
dc.publisherIEEEen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/SIU.2016.7496063en_US
dc.source.titleProceedings of the IEEE 24th Signal Processing and Communications Applications Conference, SIU 2016en_US
dc.subjectBig dataen_US
dc.subjectComputationally efficienten_US
dc.subjectNatural language processingen_US
dc.subjectRegressionen_US
dc.subjectText classificationen_US
dc.subjectTweet analysisen_US
dc.titleOnline text classification for real life tweet analysisen_US
dc.title.alternativeGerçek hayat tweet analizi için çevrimiçi metin sınıflandırmasıen_US
dc.typeConference Paperen_US

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