An Empirical Investigation of Performances of Different Word Embedding Algorithms in Comment Clustering

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dc.authoridYildiz, Tugba/0000-0002-8552-2806
dc.authorwosidYildiz, Tugba/ABC-5958-2020
dc.contributor.authorDorani, Eimal
dc.contributor.authorDuru, Nevcihan
dc.contributor.authorYildiz, Tugba
dc.date.accessioned2024-07-18T20:47:19Z
dc.date.available2024-07-18T20:47:19Z
dc.date.issued2019
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.descriptionInnovations in Intelligent Systems and Applications Conference (ASYU) -- OCT 31-NOV 02, 2019 -- Izmir, TURKEYen_US
dc.description.abstractWith the rapid growth of the usage and interest in social network services, evaluating comment clustering has become increasingly important for various commercial and scientific applications. Analyzing, organizing and ascertaining the overall theme of a large volume of comments is a challenging and time-consuming task which has attracted much attention recently. In this study, we proposed a method to address the comment clustering problem. Extensive experiments have been conducted on seven different comment datasets using TF-IDF and different word embedding algorithms, namely Word2vec, Glove and FastText; the internal clustering validation have been conducted to evaluate the performance of each method in clustering of the comments. We observed that word embedding produced significantly better results in comment clustering than TF-IDF. In addition, word2vec has shown the best performance among all; however, we found that Glove is the most stable and consistent across all datasets such that the performance improved as dataset size increased.en_US
dc.description.sponsorshipYasar Univ,IEEE Turkey Sect,Yildiz Teknik Univ,Idea,Siemensen_US
dc.identifier.doi10.1109/asyu48272.2019.8946379
dc.identifier.endpage380en_US
dc.identifier.isbn978-1-7281-2868-9
dc.identifier.scopus2-s2.0-85078347723en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage377en_US
dc.identifier.urihttps://doi.org/10.1109/asyu48272.2019.8946379
dc.identifier.urihttps://hdl.handle.net/11411/7771
dc.identifier.wosWOS:000631252400070en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartof2019 Innovations in Intelligent Systems and Applications Conference (Asyu)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectComment Clusteringen_US
dc.subjectWord2vecen_US
dc.subjectGloveen_US
dc.subjectFasttexten_US
dc.subjectTf=İdfen_US
dc.titleAn Empirical Investigation of Performances of Different Word Embedding Algorithms in Comment Clusteringen_US
dc.typeConference Objecten_US

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