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Periodic topic models in social sciences: STM a study on

Title	Periodic topic models in social sciences: STM a study on
Author	Kurnaz, A., Ünver, Yener
Publication Date:	2022
Publication Place	- IEEE
Subject	Content analysis, Social media, STM, Text mining, Topic models
Type	Document
Language	Turkish
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	978-166545092-8
Record ID	e0f13a87-bb48-4dbf-a26e-c77e6779f64e
Library Location	Law
Date	2022
Sample Text	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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Özyeğin University

Periodic topic models in social sciences: STM a study on

Author Kurnaz, A., Ünver, Yener

Publication Date 2022

Publication Place - IEEE

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

Type Document

Language Turkish

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 978-166545092-8

Record ID e0f13a87-bb48-4dbf-a26e-c77e6779f64e

Library Location Law

Date 2022

Sample Text 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