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dc.contributor.authorTekir, Selma
dc.contributor.authorSezerer, Erhan
dc.contributor.authorPolatbilek, Ozan
dc.date.accessioned2019-09-02T13:21:00Z
dc.date.available2019-09-02T13:21:00Z
dc.date.issued2019en_US
dc.identifier.citationTekir, S., Sezerer, E., Polatbilek, O. (2019). Gender prediction from tweets: Improving neural representations with hand-crafted features. Yayın için başvurusu yapılmış metin.
dc.identifier.urihttps://hdl.handle.net/11147/7251
dc.description.abstractAuthor profiling is the characterization of an author through some key attributes such as gender, age, and language. In this paper, a RNN model with Attention (RNNwA) is proposed to predict the gender of a twitter user using their tweets. Both word level and tweet level attentions are utilized to learn ’where to look’. This model1 is improved by concatenating LSA-reduced n-gram features with the learned neural representation of a user. Both models are tested on three languages: English, Spanish, Arabic. The improved version of the proposed model (RNNwA + n-gram) achieves state-of-the-art performance on English and has competitive results on Spanish and Arabic.en_US
dc.language.isoengen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc/3.0/us/*
dc.subjectRNN Modelen_US
dc.subjectDatasetsen_US
dc.subjectModel architectureen_US
dc.subjectNeural network-based modelsen_US
dc.subjectNeural representationsen_US
dc.titleGender prediction from tweets: Improving neural representations with hand-crafted featuresen_US
dc.typearticleen_US
dc.contributor.authorID0000-0002-0488-9682en_US
dc.contributor.institutionauthorTekir, Selma
dc.contributor.institutionauthorSezerer, Erhan
dc.contributor.departmentIzmir Institute of Technology. Computer Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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