Sosyal bilimlerde dönemsel konu modelleri: STM üzerine bir çalışma

Name: Sosyal bilimlerde dönemsel konu modelleri: STM üzerine bir çalışma
Author: Kurnaz, A., Ünver, Yener

İsim	Sosyal bilimlerde dönemsel konu modelleri: STM üzerine bir çalışma
Yazar	Kurnaz, A., Ünver, Yener
Basım Tarihi:	2022
Basım Yeri	- IEEE
Konu	Content analysis, Social media, STM, Text mining, Topic models
Tür	Belge
Dil	Türkçe
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-166545092-8
Kayıt Numarası	e0f13a87-bb48-4dbf-a26e-c77e6779f64e
Lokasyon	Law
Tarih	2022
Örnek Metin	Topic models are rapidly becoming popular in social sciences. However, researchers should pay attention to some critical steps while using these models. The format and content of the textual data, language, existence of covariates, and preprocessing steps are the most crucial elements of a topic model analysis. This study inspects the effect of various datasets and preprocessing steps on Structural Topic Models (STM). Results shows that preprocessing, which depends on the research question, profoundly affects the model performance. Besides, the existence of multilingual data weakens the topic quality. Also, the algorithm performance is different among long and short texts. Last, the potential usage of covariates in the model enhances its functionality in social science.
DOI	10.1109/SIU55565.2022.9864923

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Sosyal bilimlerde dönemsel konu modelleri: STM üzerine bir çalışma

Yazar Kurnaz, A., Ünver, Yener

Basım Tarihi 2022

Basım Yeri - IEEE

Konu Content analysis, Social media, STM, Text mining, Topic models

Tür Belge

Dil Türkçe

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-166545092-8

Kayıt Numarası e0f13a87-bb48-4dbf-a26e-c77e6779f64e

Lokasyon Law

Tarih 2022

Örnek Metin Topic models are rapidly becoming popular in social sciences. However, researchers should pay attention to some critical steps while using these models. The format and content of the textual data, language, existence of covariates, and preprocessing steps are the most crucial elements of a topic model analysis. This study inspects the effect of various datasets and preprocessing steps on Structural Topic Models (STM). Results shows that preprocessing, which depends on the research question, profoundly affects the model performance. Besides, the existence of multilingual data weakens the topic quality. Also, the algorithm performance is different among long and short texts. Last, the potential usage of covariates in the model enhances its functionality in social science.

DOI 10.1109/SIU55565.2022.9864923