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Roman Egger, Joanne Yu
The richness of social media data has opened a new avenue for social science research to gain insights into human behaviors and experiences. In particular, emerging data-driven approaches relying on topic models provide entirely new perspectives on interpreting social phenomena. However, the short, text-heavy, and unstructured nature of social media content often leads to methodological challenges in both data collection and analysis. In order to bridge the developing field of computational science and empirical social research, this study aims to evaluate the performance of four topic modeling techniques; namely latent Dirichlet allocation (LDA), non-negative matrix factorization (NMF), Top2Vec, and BERTopic. In view of the interplay between human relations and digital media, this research takes Twitter posts as the reference point and assesses the performance of different algorithms concerning their strengths and weaknesses in a social science context. Based on certain details during the analytical procedures and on quality issues, this research sheds light on the efficacy of using BERTopic and NMF to analyze Twitter data.
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Egger, R., & Yu, J. (2022). A Topic Modeling Comparison Between LDA, NMF, Top2Vec, and BERTopic to Demystify Twitter Posts. Frontiers in Sociology, 7, 886498. https://doi.org/10.3389/fsoc.2022.886498
Egger, Roman, and Joanne Yu. "A Topic Modeling Comparison Between LDA, NMF, Top2Vec, and BERTopic to Demystify Twitter Posts." Frontiers in Sociology, vol. 7, 2022, pp. 886498, https://doi.org/10.3389/fsoc.2022.886498.
Egger, Roman, and Joanne Yu. "A Topic Modeling Comparison Between LDA, NMF, Top2Vec, and BERTopic to Demystify Twitter Posts." Frontiers in Sociology 7 (2022): 886498. https://doi.org/10.3389/fsoc.2022.886498.
Egger R., Yu J. A Topic Modeling Comparison Between LDA, NMF, Top2Vec, and BERTopic to Demystify Twitter Posts // Frontiers in Sociology. — 2022. — Vol. 7. — P. 886498. — URL: https://doi.org/10.3389/fsoc.2022.886498
@article{egger2022topic,
title = {{A Topic Modeling Comparison Between LDA, NMF, Top2Vec, and BERTopic to Demystify Twitter Posts}},
author = {Roman Egger and Joanne Yu},
journal = {Frontiers in Sociology},
year = {2022},
volume = {7},
pages = {886498},
doi = {10.3389/fsoc.2022.886498},
url = {https://doi.org/10.3389/fsoc.2022.886498}
}TY - JOUR AU - Egger, Roman AU - Yu, Joanne TI - A Topic Modeling Comparison Between LDA, NMF, Top2Vec, and BERTopic to Demystify Twitter Posts JO - Frontiers in Sociology VL - 7 SP - 886498 PY - 2022 DA - 2022/05/06 DO - 10.3389/fsoc.2022.886498 UR - https://doi.org/10.3389/fsoc.2022.886498 ER -
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