Quantum mechanics

Context (archaeology)

World Wide Web

Computational sociology

Pure mathematics

Biology

Social psychology

Field (mathematics)

Eigenvalues and eigenvectors

Computer science

Topic model

Paleontology

Non-negative matrix factorization

Physics

Psychology

Mathematics

Social media

Sociology

Data science

Artificial intelligence

Strengths and weaknesses

Matrix decomposition

Latent Dirichlet allocation

Frontiers in Sociology

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.

A Topic Modeling Comparison Between LDA, NMF, Top2Vec, and BERTopic to Demystify Twitter Posts