Details

Autor(en) / Beteiligte

• Ergul, Halil Ibrahim
• Terzioglu, Aysecan
• Tercan, Murat
• Yanikoglu, Berrin
• Arin, Inanc

Titel

LDA Topic Modeling on Twitter Data Concerning Immigrants and Refugees

Ist Teil von

• 2023 31st Signal Processing and Communications Applications Conference (SIU), 2023, p.1-4

Ort / Verlag

IEEE

Erscheinungsjahr

2023

Quelle

IEEE Xplore

Beschreibungen/Notizen

• In this study, the attitudes and opinions of Twitter users in Turkey towards immigrants have been examined to see how people express their thoughts and opinions about immigrants in Turkey and whether there are any dominant and interpretable topics that emerge. After a comprehensive pre-preprocessing, latent themes in the tweets are discovered using the Latent Dirichlet Allocation (LDA) topic modeling methodology. As the result of this analysis, 14 topics have emerged as meaningful and interpretable. The study is done over a small dataset and is somewhat limited; however, the results can shed light on the perspectives of Twitter users towards immigrants and refugees.