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
